Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Authors: Jiyuan Shi
Xinzhe Liu
dewei wang
ouyang lu
Sören Schwertfeger
Chi Zhang
Fuchun Sun
Chenjia Bai
Xuelong Li
Venue: NeurIPS 2025 poster ()
Keywords: Humanoid Robots
Locomotion
Loco-Manipulation
Reinforcement Learning
Primary area: reinforcement_learning
TL;DR: We propose an adversarial policy learning framework for humanoid robots, aiming for  precise motion tracking of the upper body and robust locomotion of the lower body.

Abstract: Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI), a novel framework that enables adversarial policy learning between upper and lower body. Specifically, the lower body aims to provide robust locomotion capabilities to follow velocity commands while the upper body tracks various motions. Conversely, the upper-body policy ensures effective motion tracking when the robot executes velocity-based movements. Through iterative updates, these policies achieve coordinated whole-body control, which can be extended to loco-manipulation tasks with teleoperation systems. Extensive experiments demonstrate that our method achieves robust locomotion and precise motion tracking in both simulation and on the full-size Unitree H1-2 robot. Additionally, we release a large-scale whole-body motion control dataset featuring high-quality episodic trajectories from MuJoCo simulations. The project page is https://almi-humanoid.github.io.

Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Jiyuan Shi!*
Xinzhe Liu 1,2+
Dewei Wang 1.3+
Ouyang Lu 1,4 Sören Schwertfeger
Chi Zhang'
Fuchun Sun
Chenjia Bai't
Xuelong Li
Institute of Artificial Intelligence (TeleAD), China Telecom University of Science and Technology of China
ShanghaiTech University
werthhweterrn Polytechnical University
"Tsinghua University
Abstract
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI), novel framework that enables adversarial policy learning between upper and lower body. Specifically, the lower body aims provide robust locomotion capabilities to follow velocity commands while the upper body tracks various motions. Conversely, the upper-body policy ensures effective motion tracking when the robot executes velocity- movements. Through iterative updates, these policies achieve coordinated whole-body control, which can be extended to loco-manipulation tasks with telcoperation systems. Extensive experiments demonstrate that our method achieves robust locomotion and precise motion tracking in both simulation and the full-size Unitree H1-2 robot. Additionally, we release a large-scale whole-body motion control dataset featuring nigh-quality episodic trajectories from MuJoCo simulations. The project page is htttt::///// / /almii humanoid. gitggt..hbbtt.. io
Introduction
Humans exhibit diverse and expressive whole-body movements in various activities 2] For example, in dancing, humans depend on the lower body for stable movements and gait control, and the upper body executes precise actions to accomplish specific movements. This coordination allows for expressive and purposeful motions that are highly adaptable to different situations. However, achieving human-like whole-body coordination remains highly challenging task for humanoid robots. Current methods employ motion re-targeting and Reinforcement Learning (RL) to learn a whole-body policy that can track human motions by taking the tracking errors as rewards and optimizing a whole-body policy to maximize such rewards [3 4
However, this kind of approach has significant limitations. First, due the high number of Degrees of Freedom (DoF) in the humanoid robot, directly learning a whole-body control policy demands complex reward structure and makes the training process highly expensive. Second, the differences between various motions, along with some human movements beyond a robot's physical capabilities, make it difficult for the RL policy to converge. practice, since such whole-body learning approaches
Equul Contribution
'Coresponnencce to: Chenjia Bai (baiej@chinntteeccom..))
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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prioritize precise motion tracking over balance maintenance, the policy often neglects the fundamental need for robot stability. Meanwhile, the poor stability of the lower body in turn affects the execution of upper-body motion and reveals challenges in real-world deployment, such as frequent robot falls. We identify that the main reason that above methods do not separately consider the unique roles of the upper and lower bodies in motion learning, specifically, the lower body's 's role in robust locomotion and the upper body's ' task of precise motion imitation.
In this paper, we propose a novel framework, named Adversarial Locomotion and Motion Imitation (ALMI). that separately learns robust locomotion and motion imitation policies for the upper and lower bodies, respectively. Specifically, the lower body learns to follow different velocity commands while withstanding adversarial disturbances from the upper body, which leads to robust locomotion even when the upper- body movements significantly disrupt the stability. Conversely, the upper body learns to track reference motions accurately despite adversarial disturbances caused by lower-body instabiliiy,, which leads to expressive motion imitation even when the lower body is commanded to move rapidly over uneven terrain. Through iterative updates of the upper and lower policies, these policies achieve coordinated whole-body control, which can be extended to loco-manipulation tasks with open-loop upper-body control via teleoperation systems. Unlike recent works [5 that adopt separate control for the upper and lower body, ALMI involves an adversarial training process to obtain coordinated behavior, which ensures the upper and lower policies converge to an equilibrium under mild conditions. Extensive experiments demonstrate that ALMI achieves robust locomotion and precise motion tracking in both simulation and on a full-size Unitree H1-2 humanoid robot.
Further, we construct large-scale whole-body control dataset, named ALMI-X featuring episodic trajectories for the Unitree H1-2 robot in MuloCo simulations. Each episode generated by the ALMI-acquired policy, where the lower body is controlled by the velocity command, and the upper body is controlled by joystick commands (i.e., desired joints) of the reference motion from the AMASS dataset [8). We annotate language descriptions for each episode data according to both commands of the lower and upper bodies, e.g. moving backward slowly and wave the left hand. The ALMI-X dataset contains more than 80K trajectories with text commands and corresponding trajectories. We also give preliminary attempts to train a foundation model from the collected ALMI- X data, which serves an end-to-end whole-body control policy by leveraging a Transformer-bes based architecture.
Our main contributions are summarized as follows: (i) We propose novel adversarial training frame- work (ALMI) for robust locomotion and precise motion imitation for humanoid robots, addressing the distinct roles of upper and lower body through separate policy learning. (ii) We create the first large-scale whole-body control dataset (ALMI-X) that integrates language descriptions with robot trajectories, facilitating the training of foundation models for humanoid whole-body control. (iii) We conduct extensive experiments to verify the effectiveness of the ALMI policy and the preliminary study for the humanoid foundation control model in both the simulation and the real world.
2 Preliminaries
In our work, we adopt an adversarial training framework for the policy learning of both the upper and lower bodies. Specifically, we consider an MDP M = (S, A", T.,," ,,",",,,""" PP), where the lower and upper bodies share the same state space while having different action space, that is, A' and A", respectively. r' is the command-followinn and rd is the tracking reward for motion-imitation policy (i.e., the upper body). P(s'|s, a', a²) the transition probability function that denotes the probability of transitioning to the next state s' given the current state and the actions of both players. We aim to learn two policies, i.e., of and T", that control different actions for the lower and upper bodies, respectively.
reward for the locomotion policy (i.e., the lower body).
We adopted the full-sized Unitree H1-2 robot in our work, which has 27 DoFs in total, and the policy controls 21 DoFs, excluding the 3 DoFs in each wrist of the hands. The state space is defined as s = prop , _., &..g" where x. dorop [q:, qt. wi,gu, af-1] is the proprioception of the robot, c¹ = [ūz,e, byges @yaw.e] is the velocity command for the lower-body, and d, E IR² is the phase parameter at each time step, and g" E R is the reference joint position for the upper body. In proprioception, the notation q. E 1R q: TR2 w, E R2, gu, E R3, at E R12 is the joint position, the joint velocity, the angular velocity of the base, the projected gravity of the base, and the last action of the lower body, respectively.
2

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For the lower body, the policy x' gives an action a' E R representing target joint positions of the lower body according the proprioception, commands, and phase variables at each step. which are fed into the PD) controller to calculate the joint torques. For the upper body, policy x² takes propriaception and reference joint position as input, and the action a" E R includes target positions of the 3 shoulder joints and 1 elbow joint per arm, along with the waist yaw joint. As result, the two policies (T' and =²") control distinct action spaces of the robot, and share the same state space by masking the irrelevant commands for the different policy.
3 Methods
In this section, we first present the theoretical foundation for the adversarial learning framework, and then give practical algorithms for implementing such a framework for humanoid robots.
3.1 The Adversarial Learning Framework
Problem Formulation for learning n². We consider two-player zero-sum Markov game 9 T0 to learn a robust lower-body policy x². At each time step, the two players (i.e., n' and x choose different actions (a' and d²), and the humanoid robot executes both actions to obtain the reward and the next state. In learning n², we consider the lower body as agent, and the upper body is adversary that causes adversarial disturbances the locomotion policy. Thus, the lower-body policy receives the command-following reward as r'(s, a', a)), and the upper-body policy obtains a negative reward -r'(s, a', a."). Formally, the value function V V'''', x²) is defined as
V (x, x T
"=)=="===>>> == E.',, ,T Σ ('( a', a 100
where 1r is the under the trajectory distribution induced by T' and π.. Then we have the value function (= ; m²) = [V'(pVV(sss ,², =²)], which is defined by the expectation of accumulated locomotion reward r'. Then the locomotion policy n' is learned to maximize the value function to better follow commands in locomotion, while the upper body policy tries to minimize the value function, aiming to provide an effective disturbance to help learn robust locomotion policy. Formally, the two players form Markov game and there exists Nash equilibrium such have
(T ;-") max min (7 ,.)
x!
To solve this max-min problem, we adopt an independent RL optimization process for both players. Specifically, the agent obtains [[sp, dg. ry).... (ST, a'y, }1),. and adversarial obtains [(nn r.)... (ST, az; r}]] by executing each policy in the game to sample a trajectory, where each player oblivious to the actions of the other player. The two players optimize their policies independently with policy gradients using their own experiences. Then the following theorem holds.
Theorem 3.1. Given e O, suppose each policy has E-greedy exploration scheme with factors of Ea X and ex x é2, under a specific two-timescale rule of the two players' learning-rate following the independent policy gradient, we have
max Va m²) E x²
N Σ
min Min (2 s()))t Se
after N episodes, which results in c-approximate Nash equilibrium.
Intuitively, the max-min game for the lower and upper bodies leads to a robust locomotion policy (by maximizing V in the outer loop) even when the upper-body movements significantly disrupt the whole-body balance (by minimizing V in the inner loop). This adversarial learning process is guaranteed to converge to a e-approximate Nash equilibrium according to Theorem 3.1
Problem Formulation for learning n". Learning a precise motion imitation policy n² follows a similar process by considering n" as the agent and X² as the adversary. In the Markov game, the upper body receives a motion tracking reward r" r"(s, a', aa)) and the lower body receives (s, a"", which aims to give adversarial disturbances for motion tracking. Then we define V* n r") ==
Ex','"
Σi 7 (Be, a', aa)) so = s) and Vi (m',TT)) T²) = Ee-p (V T', ('),),,,)))).+))) Then the Markov
game is formulated
p (T x=")== = max min Vid (TT', 7)
(4)
3

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Iulo
Value p
w. PPO
Upper body (ol)
(a' m ggt) .eoneninn
Pootocoptoc
(al adve
mycon
Conmard
intrrctt
adversend motion siae
inversarial
Interaal
Prinlage Info
command
It aa grop 2,)
(at, dlav)
Prgrooptoon Command
command
(a lo ,ly))
Velue
Lowerbody DeF)
update pokay w. PPO
Reward pt
Istan sinsulation
(a) Learning upper-body policy
Iterative updates
(b) Learinng lower-body policy
Figure I: The overview of ALMI. (a) In updating the upper-body policy n²,, we sample adversarial velocity command cadv and obtains a'dv Then use (a" d'ayy) to interact with the environ to collect experiences, which are used to update x via PPO algorithm [T. (b) Similarly, in updating the lower-body policy, sample adversarial motion Pädv and obtains ady-- Then use (a agav) to interact and update x!. The two policies (x',T") finally converge via multiple mutual iterations.
By performing independent policy gradient optimization for the two players, we can prove that such process also results in c-approximate Nash equilibrium.
Simplified Formulation. According to Eq. 2 and 4 we require two pairs of (T'' x²) to optimize V and V since they are defined by the opposite max-min problem, which could be computationally expensive. To address this, we propose simplified framework by learning a single paired policy (T), Specifically, (i) in learning the locomotion policy n². we optimize the parameters of n¹ while keeping the upper-body policy π fixed. However, we sample an adversurial motions with a designed curriculum from the motion dataset, and set the corresponding reference joint position Jndv as the condition of т (- prop gudv) to generate adversarial actions andy. Similarly, (ii) in learning the motion imitation policy π², we only update n² and keep the lower-body policy n' fixed, Then, we sample an adversarial command ct with the curriculum for the locomotion policy n' r-(lsprrp c(.-) to generate a' a'avv- To conclude, we change the inner-loop optimization of the max-min problem (in Eq. 2 and (4) from the parameter space to the command space (i.c., by sampling g and clav). which leads practical efficient algorithm. Fig. gives an overview of our method.
3.2 Robust Locomotion for the Lower Body
Achieving robust locomotion in humanoid robots remains a significant challenge, primar- ily due to the dynamic nature of environments and the inher- ent complexity of multi-joint floating base systems. This instability is particularly pro- nounced in full-sized humanoid robots, which often require in- creased load capacity to ex- ecute whole-body control or loco-manipulation tasks. Such requirements typically lead to higher arm mass and inertia, exacerbating system instability. Consequently, during coordi- nated upper and lower body movements or mobile manipu- lation, disturbances generated by the upper body exert signifi- cant impacts on the lower body, necessitating robust policies to mitigate these effects.
Table 1: Reward terms and weights for lower policy.
Term
Expression
Weight
Penalty
TwFF position posiiisition Alive
Fimit
T(g' é [d g I[robot stays alive)
-5.0
0.15
Regulantzation
Linear velocity of Z axis Angular velocity axis Orieatation
|exx | atods
-2
-1
Tonque
l|T
-le5
Base Hoight
A target
Deff acceleration
1
2.5e-
velocity
Lower body action rate
Ma rollyyy
0.01
Hip Daf' position
g
1
-1
Sligpage
feet
feet 2
swing height.
0.0811 [[ Tort
Foet Contect
[XX]] ffeet
l2
< 0.55))
Foet distance
exp(-100 dout rangey
Knee distance
exp(-100 Aaoe
range
Stand still
l 1112 1 x x[]e lla < 0.1)
-2
Ankle toqque
| Inite 13
3e5
rate
||agukie andder
veleeity
[wl]] I1 I[]le 0.1)
Foet contact forees
min(]] flee 100
.00I
Task
Linear velocity
expl uppi-dlea dllery u
Angular velocity
exp( exp(-4] uyaw

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Motivation of Curriculum. In learning a robust locomotion policy, an adversarial up- per body component is essential to generate perturbations. However, directly training such an adversary is challenging. Empiri- cal results show that without constraints, the upper body can terminate episodes early or collide with the lower body to minimize its reward. Manually design- ing constraints is labor intensive, and the lower body can exploit flaws in the reward function, leading to poor real-world perfor- mance. Thus, simplify the original problem by sampling adversarial motions without updating the upper-body policy. Although aggressive sampling of highly dynamic motions can reduce the locomotion policy's value function (i.e. V*) as in Eq. (]). this strategy cannot adequately teach the locomotion policy how to resist disturbances when the policy is relatively weak, resulting in slow convergence. To overcome this, we introduce a novel curriculum mechanism that starts with moderate disturbances and gradually increases their intensity as the lower body's 's robustness improves, ensuring efficient learning and robustness improvement throughout training.
Table 2: Reward terms and weights for upper policy.
Torm
Expression
Weight
Penalty
DoF' position limits Alive
Iig" & [9 mmase] )>
-5.0
(robot stays alive)
0.15
Regularization
Orientation
1
roat
Torque
|
-1e55
Upper Dof' acceleration
14
-2.5e-7
Upper DoF velocity
1
-1e-3
Upper body action rate
||a!
.0.01
Task
Upper DoF position
exp( exp(-0.5|| -0.5
q" (13))
10
discussed in $3.1 we
Dual Curriculum Mechanism. We propose a novel dual curriculum mechanism for adversarial motion sampling by scoring upper-body motions based their impact the lower-body's stability. Specifically, we initially train a basic locomotion policy To without any upper body intervention. Then we use PD controller (in the first round) or π control the upper-body to track motions from the re-targeted AMASS [8] dataset in simulation, while the lower body follows the basic velocity commands. The robot inevitably falls initially due to upper body perturbations, and we record survival length (denoted as 1a1 E [0, (0, (iaa)) as the primary metric for motion difficulty. Then, motions are sorted into a list M = [M,...., Mi] by increasing difficulty based on survival length. Further, we introduce factor, motion scale as E [0, 1). to scale the target joint positions of the upper body as
(5) For each iteration, the robot is assigned a window of motions [May: MMa+ww] from M. where w is the window size and ay = 1 at the start. These motions, scaled by atg, are used to control the upper body to provide disturbances. The lower-body policy ni is updated πI < mb. through an RL optimization process with locomotion-relas difficulty of sampled motions, we calculate mean survival length (denoted as pussl of the sampled motions as a metric to update dn and ag, which progressively increases motion difficulty based on the current policy's disturiiter capability. Formally,
Burges = q + (g g
rewards detailed in Table To adaptively adjust the
min((aa + w. MM| - w), if I 2 0.8 * [el otherwise
Od max((a 2w, 0),
which increases the difficulty if x² can effectively resist the interference caused by sampled motions, and decreases the difficulty otherwise. The update of motion scale follows a similar process as
min(a, + 0.05, 1), ad M 0g5 max(a, 0.01, 0), ad
which signifies that the motion scale a, increases after all motions at the current scale are successfully managed. However, increasing a, typically decreases [uee] potentially causing og decrease and resulting in motion sampling that reverts to previous entries in M. This triggers a new cycle of policy updates with adjusted motions and scales. To maintain relevance, the motion list M is periodically re-sorted to ensure alignment with the evolving robustness of the locomotion policy. The Algorithm in Appendix B.2 gives a training process for the locomotion policy.
3.3 Motion Tracking for the Upper Body
The upper-body policy x" aims to track various motions under disturbances from lower-body movements with adversarial commands. Before motion tracking, we follow the process proposed by
5

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ALMI- X S
Text Commandd
A:
State-Action
CLIP
Pair Vectoos
Language Deseription
MuJoCo
Duta Collection
Causal Self-Attention
Motion Selection
Upper chody
observed
Predict Nest
State Action
exectted
Pair Voctoes
Commund Selection
Lawer
32dy
Lv.2
(a) ALMI-X Collection
(b) Tramsformer- baved Foundation Policy
Figure 2: ALMI-X dataset and foundation model training. (a) ALMI-X features motion target and velocity command for the learned policies, combining with language description. (b) The foundation model learns P(@++1|$5: agi, 7) from a segment of state-action pairs via causal attention. In inference, the last action is executed based on the history and obtains the true state for next steps.
PHC [1] to re-target the human motion from AMASS (8] dataset to humanoid robot. To enhance motion smoothness, we applied low-pass filtering to the re-targeted motion data.
In the adversarial training process, we use the locomotion policy s' in the current round to control the lower body, and the upper-body policy is learned by maximizing the motion tracking reward. Different from $3.2 that requires delicate curriculum mechanism, the curriculum for adversarial n' can be relatively simple since the command c' only contains velocity commands. Specifically, cx is sampled from a range of [cminn- <max) and we adjust the value of éuin and Cmax according to the tracking error of motions in the upper body, which is affected by the movement of the lower body. In training n", the lower-body command gradually increases its difficulty based on the tracking error of the upper body. The reward design for the upper-body policy described in Table 2 and the detailed curriculum mechanism is provided in Appendix B.3
4 ALMI-X Datasets and Foundation Model
The adversarial training processes outlined in $3.2 and $3. involve an iterative procedure over multiple epochs reach convergence. The final policies are used gather a large-scale high-quality dataset, which is used to train a foundation model dedicated to end-to-end whole-body control.
Dataset Construction. We collect ALMI-X dataset in MuJoCo simulations by running the trained ALMI policy, which consists of the final policy n² and n" In this simulation, we combine a diverse range of upper-body motions with omnidirectional lower-body commands, and employ pre-defined paradigm to generate corresponding linguistic descriptions for each combination. (i) For the upper- body, we collect data using our upper-body policy track various motions from subset of the AMASS dataset [8], where we remove entries with indistinct movements or those that could not be matched with the lower-body commands, such as push from behind. (ii) For the lower-body, we first categorize command directions into several types according to different combination of linear and angular velocity command and define 3 difficulty levels for command magnitudes, then the lower-body command is set by combining direction types and difficulty levels. Overall, each upper-body motion from the AMASSS subset is paired with a specific direction type and a difficulty level serving as the inputs of n" and ní to control the robot. In addition, trajectories in which the lower body keep standing while the upper body tracks motions are also incorporated into the dataset. Each language description T in ALMI-X is organized as "S/movement mode/ S/direction/ 8/velocity level) and S/motion]". each which corresponds to the data collected from trajectory lasting average 4 seconds with 200 steps. For each T. we run two policies (i.c., n" x') based on the commands obtained from the aforementioned combinations to achieve humanoid whole-body, body control. We record the trajectory information such the robot states т, = (Su. $1...., $T) and actions T, (ag. aI...., a.). Our data exhibits high executability, and the details are discussed in Appendix C
Foundation Model for Humanoid Control. Using the ALMI-X dataset, we give preliminary attempts train whole-body foundation model with supervised learning that can execute various

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motions in response to text commands. We adopt a Transformer decoder architecture that implements causal self-atiention modules over a text command and historical state-action pairs. Unlike UH-1 [3] that predicts the entire action sequence based on the text command, our policy autoregressively models the next action by incorporating real-time interaction history between the robot and the environment. This architecture avoids executing the entire action sequence without considering the random noise and interference of the environment, instead adjusting actions based on intermediate states, ensuring robust and adaptive control. Moreover, compared to methods focused solely on walking 14, ALMI-X contains more diverse data from various motions and velocity commands, enabling the learning of the foundation model to achieve complex language-guide whole body control. The architecture is shown in Fig. 2 and the details are given in Appendix
5 Related Works
Humanoid Locomotion. Traditional humanoid locomotion primarily focuses on trajectory planning
[6]] and Zero-Moment Point (ZMP)-based gait synthesis [1]. Other methods like whole- body control [9 20 and preeccire control [2] 22 gain significant progress with dynamics
modeling, while it can be difficult to model rich contact in complex terrains [23]. Recently, learning-
based algorithms have shown promising results in humanoid locomotion tasks 22 25
RL has
become powerful tool for learning locomotion policies by enabling agents to interact with the environment through parallelized simulations [26]. Based on this, humanoids can acquire wide range of skills, including walking on complex terrains (27). (28), gait control [29]. standing up [30 31], narrow-terrain navigation [3]. jumping [33], and even parkour [34 35 However, most methods focus solely on controlling the lower-body joints, treating the upper body as fixed load (36 37
Although some studies have explored whole-body control policies [32 29 their primary objective is to learn an upper-body swinging policy to coordinate with the locomotion task. In contrast, method enables the lower-body policy to resist disturbances caused by various upper-body motions.
Humanoid Motion Imitation. Recent research has proposed expressive motion imitation algorithms that learn human-like behaviors from motion capture datasets (3| These methods employ motion re-targeting and RL-based optimization to achieve fine- fin-grained motion imitation and postural stability 41H43). However, balancing these two objectives can be challenging, as the execution of motions may disturb stability, while poor stability, in turn, affects the precision of motion execution. Several methods address this issue through careful reward design 44 45), decomposed tracking strategies 66, 4 [46 477, and residual learning [48]. Unlike these methods, our approach focuses solely on tracking humaa motions in the upper body. Although this may result in less expressiveness, it lcads to robust locomotion in the lower body and precise motion tracking in the upper body, Icading to an effective whole-body policy for real-world deployment. Other methods [49 50 5 also adopt separate control paradigm for training the two parts with different rewards, while we employ an adversarial training process to promote coordinated behaviors between locomotion and motion imitation policies.
Foundation Model for Robotics Our work is also related to the learning of foundation models for robotics. Previous works mainly focus on building jision-language-actioo models for end-to-end
manipulation, such as MT--Dfff [5T], RT-1 (52) Octo (53), VLe [54), RDT (55). To [56). GO-1
[57. and BRS (58) They rely on lcage-scale datasets collected by humans, such RH20T [59].
Bridge Data [60]. RoboMIND [61], Open-X [62). AgiBot Data [57] and BeGen-BBnnhffe (63) However, these datasets focus mainly on arm manipulation with fixed or wheeled platform. In contrast, dataset (i.e., ALMI-X) targets whole-body control in humanoid robots, which involves controlling
more DoFs than manipulation tasks. UH-1 [13 is closely related ALMI, but learns mapping
from text descriptions to keypoints, and level actions are found to be unexecutable in real robots.
6 Experiments
In this section, we evaluate the performance of ALMI using the Unitree H1-2 robot in both simulated and real-world environments. Our experiments aim to address the following research questions: Q1. How does ALMI perform in terms of tracking precision for lower-body velocity commands and upper-body motions? Q2. How does ALMI perform regarding stability and robustness in complex scenarios? Q3. What benefits does adversarial iterative training and the arm curriculum mechanism provide for policy optimization? Q4. How does ALMI perform in the real world?
7

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Training Details. Policy training is conducted within the Isaac Gym simulator utilizzig 4.096 parallel environments, We perform three rounds of adversarial iterations and evaluate the policies of the final round. Notably, during the adversarial iterative training process, the initial lower-body policy converges after approximately 10a steps. As iterations progress, the number of steps required for convergence decreases significantly. The total duration of the three training iterations is approximately 17 hours. For each baseline method, we use the checkpoint obtained after training convergence for testing. To enhance generalization, we implement domain randomization for sim-to-sim and sim-to-real transfers, with specific details provided in Appendix B35
Evaluation Metrics. For simulated evaluation, we adopt the high-quality CMU MoCap dataset [64] with 1122 motion clips (denoted as Demu)) to evaluate different metrics in IsaacGym [26.. The metrics include (i) the mean linear velocity enror Evel (m/s) and the mean angular velocity error Fang (rad/s) that evaluate the command tracking accuracy: (ii) the upper body joints position error P (m) and key points error Plape (m) that evaluates the motion tracking accuracy; (iii) the joint
difference for both parts Eupper (rad), Eection (rad); (iv) and the projected gravity Eg evaluate the stability of policy. (v) We also statistically analyze survival rale to assess the robustness of the policy.
follows: (i) Exbody ( which employs unified policy to control whole-body joints, tracking upper-bode -pder-bddy movements from motion data and lower-body root motion via command sampling. (ii) ALMI (whole), which uses a single policy to control the upper and lower bodies, with identical reward functions for training each part as in ALMI. (iii) ALMI (w/o curriculum). An ablation study that omits the arm curriculum, where the policy loads the motion randomly and the motion scale ag is set to 1. (iv) ALMI (w/o adv. learning). This study evaluates the impact of our iterative adversarial training by testing policies from the first and second rounds. We also compare our method with approaches that simultaneously imitate both upper and lower body movements. We consider two state-of-the-ard methods: (v) ExBody2 (4), which leverages privileged teacher policy to distill precise mimicry skills into student policy, enabling whole-body imitation, and (vi) OmniH20 [5], an imitation- based whole-body control method that supports real-time input from various input devices.
Baselines. Our baselines
6.1 Main Result of ALMI
To evaluate locomotion and motion tracking capabilities, calculate metrics in Demu using mands in the lower body categorized into difficulty levels easy, medium, and hard, as described in Table These levels encompass linear velocities in the and y directions, angular veloc- ity in the yaw direction, terrain level in the Isaac Gym and the presence of external forces. To control variables and facilitate analysis, we assess linear and angular velocities separately (set- ting the other to zero during testing) and report the average metrics.
Table 3: Locomotion difficulty level setting.
To answer Q1 and Q2, we give the comparative results in Table 4 (i) Tracking accuracy. ALMI demon- strates superior tracking precision across all difficulty levels for both
Command & Environment
Level
0x,t
By.
terrain level
push robot
easy
0.7
0.0
0.2
medium
1.0
0.3
0.4
hard
1.3
0.6
0.6
Table 4: Simulated evaluation of ALMI, ALMI (whole body) and Exbody on CMU dataset.
Metries
Method
Evel 4
Fang +
puppper +
pupper
E action
F actioo
+
Fg 4
Survival 1
Easy
ALMI
0,1135
0.2647
0.1931
0.0460
0.0462
0.0170
0.6919
1.0000
ALMI(whole)
0.1386
0.5433
0.5756
0.0704
0.0800
3.0356
0.9675
0.9991
Exbody
0.2383
0.4056
0.3559
0.0995
1.7813
1.8152
0.9693
0.8912
Medium
ALMI
0.2192
0.3520
0.2047
0.0450
0.0598
0.0172
0.7604
0.9852
ALMl(whole)
0.2380
0.5563
0.6734
0.0637
0.0409
2,9225
1.0750
0.9763
Exhody
0.3063
0.5087
0.3658
0.1233
1.7683
1.8019
1.0166
0.8845
Hard
ALMI 0.2202
0.4812
0.2116
0.0458
0.0600
0.0175
0.8551
0.9723
ALMl(whole)
0.3178
0.7224
0.7022
0.0635
0.0519
2.9317
1.1656
0.9491
Exhody
0.4838
0.5753
0.3758
0.1
1.7352
1.7689
1.0243
0.8778
8

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Table 5: Comparison with Exbody2 and OmniH20 on GI simulation.
Metrics
Method
Evsl +
Eang 4
Pipeer 4
pupppr kppe
E pupper actioo
Elower + action
Eg J
Survival
ALMI
0.1396
0.2776
0.2367
0.0411
0.0198
0.7411
0.0977
0.9484
OmaiH20
0.1615
0.4166
1,0826
0.0598
1.2773
2.3219
0.1696
0.3882
Exbody2
0.4015
0.6066
0.3821
0.0719
1.1797
1.3547
0,3367
0.8848
upper-bodi motions and lower- body velocity commands, consistently outperforming other baselines. The results highlight that single policy struggles with upper-lower body coordination, limiting tracking accuracy. In contrast, our adversarial training framework simultaneously enhances the accuracy of both policies for their respective objectives. (ii) Stability and robustness. According to the survival rate, it can be found that with improvement in difficulty levels, ALMI track motions and velocity commands robustly and effectively prevent falls. This stability is attributed to the lower-bode body policy, which effectively learns to execute stable movements despite adversarial upper-body interference, underscoring the efficacy of adversarial training strategy.
As OmniH20 and Exbody2 are imitation-based whole-body control methods without velocity track- ing, for ALMI, we use linear and angular velocity provided by the reference motion as velocity tracking command: for OmniH20 and Exbody2, they directly track the reference motions. This experiment is conducted without terrain or push. To indicate that our method can be extended to other robot platforms, be observed that our method generalizes well across different robotic platforms and demonstrates superior robustness compared to imitation- based whole-body control methods.
Unitree G1 robot in this setting. The results shown in 5 It can
To answer Q3, we evaluate the impact of our arm curriculum and adversarial learning techniques, with results presented in Table 6 We use the trained lower-body policies across three iterations, labeled lower-l, 1, lower-2, and lower-3. Under easy velocity commands without environmental disturbances, performance differences among the iterations minimal. However, as task complexity increased, lower-3 demonstrated superior performance across all metrics compared to earlier iterations, while lower-7 showed notably inferior results. During adversarial training, the lower and upper policies iteratively refined their motion and command tracking capabilities while generating progressively larger disturbances for each other, guided by the designed curriculum. For instance, to achieve high-velocity commands, the lower body might execute large- amplltude actions, inducing oscillations that disrupt upper-body movements, and vice versa. Through this iterative process, both policies gradually adapt to adversarial perturbations, eventually reaching a stable condition. In addition, the critical role of the curriculum. By systematically introducing upper-body motions from easy to hard in training, the lower-body policy effectively learned to handle increasingly difficult perturbations and motion scales.
Table 6: Ablation studies of adversarial training technique and arm curriculum in ALMI.
Metries
Method
Evel 1
Fang +
puppper
Eupper
E action
Paction +
Eg
Survival
Easy
lower:3 upper-2
0.1135
0.2647
0.1931
0,0460
0.0462
0,0170
0.6919
1.0000
lower-2 upper-2
0.1164
0.2669
0.1955
0.0452
0.0475
0.0171
0.7121
1.0000
lower-1 upper-2
0.1271
0.2738
0.1928
0.0526
0.0642
0.0171
0.7052
1.0000
w/o arm curriculum
0.1411
0.2726
0.1924
0.0504
0.0618
0,0172
0.7472
0.9995
Medium
lower-3 upper-2
0.2192
0.3520
0.2007
0.0450
0.0598
0.0172
0.7604
0.9852
lower-2 + upper-2
0.2213
0.3571
0.2033
0.0458
0.0607
0.0172
0.7748
0.9772
luwer-1 upper-2
0.2262
0.3872
0,2173
0.0492
0.0604
0.7730
0.9273
w/o arm curriculum
0.2571
0,4348
0.2068
0.0476
0.0601
0.0173
1.0587
0.9652
Hard
lower-3 upper-2
0.2202
0.4812
0.2116
0.0458
0.0600
0.0175
0.8551
0.9723
lower-2 upper-2
0.2892
0.5395
0.2231
0.0482
0.0645
0.0178
0.9479
0.9233
lower-1 upper-2
0.2566
0,5172
0.2451
0.0537
0.0777
0.0179
0.9462
0.8743
w/o arm curricutum
0.3658
0.6398
0.2394
0.0461
0.0726
0.0180
1.2042
0.8480

---

(a) Salute
(b) Wave
(c) Shake hand
(d) Punch
Figure 3: The sim-to-real comparison of humanoid robot in tracking various motions.
6.2 Result of Foundation Model
The Transformer-basee foundation model leverages textual descriptions of the whole-body motion along with historical information predict future action. experiments, we find that when training on subset of the ALMI-X dataset (e.g., waving-related data), the model converges to robust policy, enabling execution of various waving and moving motions in the MuJoCo simulations. We highlight that this is particularly challenging, as no prior works show a purely offline policy can control full- mill-sized humanoid robots to generate language-guin guided behaviors. However, when we extend the training data to the complete ALMI-X, the performance degraded, indicating limitations of the current model in handling highly diverse behaviors. ALMI-X dataset and our finding provide promising starting point for future exploration, with detailed results provided in Appendix D
6.3 Real- World Experiments
To answer Q4, we deploy ALMI the Unitree H1-2 robot evaluate real-world performance. For lower-body control, we use which controls the robot to achieve omnidirectional movement. For upper body control, can adopt open-loop controller or ALMI policy to track various motions, as well as using VR devices to support dexterous control in doco-manipulation tasks. The details are given in Appendix
mounted remote controller to send velocity commands to x',
To evaluate the capabilities in maintaining stability movements while tracking motions precisely, we control the robot to execute various upper body motions while standing walking in omnidirection. Fig. 3 illustrates the motion tracking behavior, emphasizing the alignment between simulation and real-world performance. To further demonstrate the robot's capability perform complex motions during locomotion, Fig. [8 in the appendix provides sim-to-real comparison of the robot completing a full motion sequence while moving forward and recovering standing position. These experiments confirm that ALMI enables the real robot to achieve robust locomotion and accurate motion tracking.
7 Conclusion
This paper presented ALMII, an adversarial training framework for humanoid whole-body control. ALMI leverages the designed curriculum for upper and lower body training, achieving stable lo- comotion and precise motion imitation through iterative policy updates. Our theoretical analysis demonstrates ALMI's effectiveness within two-player Markov game framework, supported by practical algorithms for implementation. The learned policy used to collect the ALMI-X dataset with language annotations, facilitating foundation model training for the research community. Empir- ical results highlight ALMI's superior performance in both simulated environments and real-world deployments. However, approach still has limitations. The adversarial framework of ALMI, albeit practical and robust for mobile manipulation, performs suboptimally in highly dynamic whole- body tasks like dancing; furthermore, our foundation model remains exploratory, with significant headroom for enhancing data efficiency. Future works include enhancing whole-body coordination through unified task-oriented rewards and improving the foundation model using advanced model architectures.
10

---

Acknowledgments
This work is supported by the National Key Research and Development Program of China (Grant No.2024YFE0210000) the National Natural Science Foundation of China (Grant No.62306242. the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2024QNRC001), and the Yangfan Project of the Shanghai (Grant No.23YF11462200)
References
[1] Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang, Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, and Siyuan Huang. Move you say interact you can: Language-guided human motion generation with scene affordance. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 433 2024.
[2] Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen. Motiongpt: Human motion as a foreign language. Advances in Neural Information Processing Systems, 36:20067-20079, 2023.
[3] Zipeng Fu, Qingqing Zhao, Qi Wu, Gordon Wetzstein, and Chelsea Finn. Humanplus: Humanoid shadowing and imitation from humans. In 8th Annual Conference on Robot Learning, 2024.
[4] Mazeyu Ji, Xuanbin Peng, Fangehen Liu, Jialong Li, Ge Yang, Xuxin Cheng, and Xiaolong Wang. Exbody2: Advanced expressive humanoid whole-body control. arXiv preprint arXiv: 13/96, 2024.
[5] Chenhao Lu, Xuxin Cheng, Jialong Li, Shiqi Yang. Mazeyu Ji, Chengjing Yuan, Ge Yang. Sha Yi, and Xiaolong Wang. Mobile-television. Predictive motion priors for humanoid whole-body control. In IEEE International Conference on Robotics and Automation, 2025.
[6] Xuxin Cheng, Yandong Ji, Junming Chen, Ruihan Yang. Ge Yang. and Xiaolong Wang. Expressive whole-body control for humanoid robots. In Robotics: Science and Systems, 2024.
[7] Qingwei Ben, Feiyu Jia, Jia Zeng. Junting Dong, Dahua Lin, and Jiangmiao Pang. HOMIE: Humanoid Loco- Maco-Maaipulttion with Isomorphic Exoskeleton Cockpit. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025, doi: 10. 10007//SS..2225.XXX
[8] Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black. Amass: Archive of motion capture as surface shapes. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5442 5451, 2019.
[9] Michael L Littman. Markov games as a framework multi-agent reinforcement learning. In Muchine learning proceedings 1994, pages 157-163. Elsevier, 1994.
[10] Julien Perolat, Bruno Scherrer, Bilal Piot, and Olivier Pietquin. Approximate dynamic programming for two-player zero-sum markov games, In International Conference on Machine Learning, pages 132991329 PMI.R, 2015.
[11] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov, Proximal policy optimization algorithms. arXiv preprint OrXiv:1707.06344 2017.
[12] Zhengyi Luo, Jinkun Cao, Kris Kitani, Weipeng Xu, et al. Perpetual humanoid control for real-time simulated avatars. In Proceedings of the IEEE/CVF International Conference Computer Vision, pages 10895-10900 10904, 2023.
[13] Jiageng Mao, Siheng Zhao, Siqi Song, Tianheng Shi, Junjie Ye, Mingtong Zhang, Haoran Geng, Jitendra Malik, Vitor Guizilini, and Yue Wang. Learning from massive human videos for universal humanoid pose control. arXiv preprint A.V::2412.14172.. 2024.
[14] Ilija Radosavovic, Bike Zhang, Baifeng Shi, Jathushan Rajasegaran, Sarthak Kamat, Trevor Darrell, Koushil Sreenath, and Jitendra Malik. Humanoid locomotion as next token prediction. The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
[15] Ruben Grandia, Fabian Jenelten. Shaohui Yang. Farbod Farshidian, and Marco Hutter. Perceptive locomo- tion through nonlinear model model-predictive control. JEEE Transactions on Robotics, 39(5):3402-3411, 2023. doi: 10.1 TO9/TR...023.3275..2
[16] Avadesh Meduri, Paarth Shah, Julian Viereck, Majid Khadiv, loannis Havoutis, and Ludovic Righetti. Bicommp: A nonlinear model predictive control framework for whole body motion planning. IEEE Transactions Robotics, 39(2):905 922, 2023. doi: 10. 200.3TRR...22336888888

---

[17] M. Vukobratovic and J. Stepanenko. On the stability of anthropomorphic systems. sciences, 15:1-37. 1972.
Mathematical Bio-
[18] M. Vukobratović, B. Borovac, D. Surla, and D. Stokić. Biped Locomotion: Dynamics, Stability, Control and Application Peringer-Verlag. Berlin, 1990.
[19] Luis Sentis and Oussama Khatib. A whole-body control framework for humanoids operating in human environments. In JEEE International Conference on Robotics and Automation, pages 2641-26646 Orlando, FL, USA, 2006, IEEE.
[20] Koenemann, A. Del Prete, Y. Tassa, E. Todorov. O. Stasse, M. Bennewitz, and Mansard. Whole-body model-predietive control applied to the HRP-2 humanoid. In JEEE/RSJ International Conference on Intelligent Robots and Systems, pages $346-3351, 2015.
[21] Junheng Li and Quan Nguyen. Multi-contact mpc for dynamic loco-manipulation on humanoid robots. In American Control Conference (ACC), pages 1215-1220. IEEE, 2023.
[22] Jean-Pierre Sleiman. Farbod Farshidian. Maria Vittoria Minniti. and Marco Hutter. A unified mpe framework for whole-body dynamic locomotion and manipulation. IEEE Roboties and Automation Letters, 6(3):488-4669) 2021. doi: (0.1109/LAA.2221306669
[23] Gerrit Schultz and Katja Mombaur. Modeling and optimal control of human- like running. IEEE/ASME Transactions on Mechatronics, 15(5):883-77 2009. doi: BLL.0/TMEEC..20002022222
[24] Qiang Zhang, Peter Cui, David Yan, Jingkai Sun, Yiqun Duan, Gang Han, Wen Zhao, Weining Zhang, Yijie Guo, Arthur Zhang. et al. Whole-body humanoid robot locomotion with human reference. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11225-11231. IEEE, 2024.
[25] Tianyu Li, Hartmut Geyer, Christopher G Atkeson, and Akshara Rai. Using deep reinforcement learning to Icarn high-level policies on the atrias biped. In 2019 International Conference Robotics and Automation (ICRA), pages 263-269. IEEE, 2019.
[26] Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint arXiv:2108, 10470, 2021.
[27] Huayi Wang, Zirui Wang. Junli Ren, Qingwei Ben, Tao Huang. Weinan Zhang, and Jiangmiao Pang. BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds. In Proceedings of Robotics: Science and Systems, Los CA, USA, June 2025. doi: 101115007RS.2023 X02.0XX8.068.
[28] Dewei Wang, Xinmiao Wang, Xinzhe Liu, Jiyuan Shi, Yingnan Zhao, Chenjia Bai, and Xuelong Li. More: Mixture of residual experts for humanoid lifelike gaits learning complex terrains, 2025. URL https://arxiv org/ /2506 08840
[29] Yufci Xue, Wentao Dong, Minghuan Liu, Weinan Zhang, and Jiangmiao Pang. Unified and General Humanoid Whole-Body Controller for Fine-Grained Locomotion. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025. doi: 10. 1566//5/SS 2025. Xx1.0.7
[30] Xialin He, Runpei Dong, Zixuan Chen, and Saurabh Gupta. Learning Getting- Seting-UUp Policies for Heal-World Humanoid Robots. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025. doi: 10.1 2.1156677SSS2222. 2025.XX1.063.
[31] Tao Huang, Junli Ren, Huayi Wang, Zirui Wang, Qingwei Ben, Muning Wen, Xiao Chen, Jianan Li, and Jiangmiao Pang. Learning Humanoid Standing-up Control across Diverse Postures. In Proceedings of Robotics: Science and Systems, LosAngeles, CA. USA. June 2025. doi: 205507//S..222..... XX1.064..
[32] Weiji Xie, Chenjia Bai, Jiyuan Shi, Junkai Yang. Yunfei Ge, Weinan Zhang, and Xuelong Li. Humanoid whole-body locomotion narrow terrain via dynamic balance and reinforcement learning. arXiv preprint arXiv:2502. 17219, 2025.
[33] Zhongyu Li, Xue Bin Peng, Picter Abheel, Sergey Levine, Glen Berseth, and Koushil Sreenath, Robust and versatile bipedal jumping control through reinforcement learning. Robotics science and systems. RSS, 2023.
[34] Junfeng Long, Junli Ren, Moji Shi, Zirui Wang. Tao Huang, Ping Luo, and Jiangmiao Pang. Learning humanoid locomotion with perceptive internal model. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pages 9997-10003. IEEE, 2025.
12

---

[35] Ziwen Zhuang, Shenzhe Yao, and Hang Zhao. Humanoid parkour learning. In 8th Annual Conference on Robot Learning.
[36] Xinyang Gu, Yen-Jen Wang, and Jianyu Chen. Humanoid-gym: Reinforcement learning for humanoid rohot with zero-shot sim2real transfer. arXiv preprint arXiv:2404.056655 2024.
[37] Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, and Jianyu Chen. Advancing humamoid locomotion: Mastering challenging terrains with denoising world model learning. In Robotics: Science and Systems, 2024.
[38] Xue Bin Peng. Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa. Amp: Adversarial motion priors for stylized physics-based character control. ACM Transactions on Graphics (ToG), 40(4), 2021.
[39] Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler. Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters. ACM Trans. Graph., 41 (4), July 2022.
[40] Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, and Xue Bin Peng. Calm: Condi- tional adversarial latent models for directable virtual characters. In ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH '23, New York, NY, USA, 2023, Association for Computing Machinery, ISBN 9798400701597, doi: 10. (0.1145/358844233559544
[41] Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris M. Kitani, and Weipeng Xu. Universal humanoid motion representations for physics-based control. The Twelfth International Conference on Learning Representations. 2024.
[42] Chen Tessler, Yunrong Guo, Ofir Nabati, Gal Chechik, and Xue Bin Peng. Maskedmimie: Unified physics-based character control through masked motion inpainting. ACM Transactions on Graphics (TOG), 2024.
[43] Mohamed Hassan, Yunrong Guo, Tingwu Wang, Michael Black, Sanja Fidler, and Xue Bin Peng. Syn- thesizing physical character-seene interactions. In ACM SIGGRAPH 2023 Conference Proceedings, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400701597.
[44] Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, and Guanya Shi. Learning human-to- to-humanoid real time whole-body teleoperation. In JEEE/RSJ International Conference on Intelligent Robots and Systems, 2024.
[45] Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris M Kitani, Changliu Liu, and Guanya Shi. Omnih2o: Universal and dexterous humnan-t--huaancn whole-body teleoperation and lcarning. In 8th Annual Conference on Robot Learning, 2024.
[46] Weiji Xie, Jinrui Han, Jiakun Zheng, Huanyu Li, Xinzhe Liu, Jiyuan Shi, Weinan Zhang, Chenjia Bai, and Xuelong L.. Kungfuhot: Physics-hased humanoid whole-body control for learning highly-dynamic skills. In Neural Information Processing Systems (NeurIPS), 2025. /tttttss:://///t :/aaxii org/ abs//506/666 12851
[47] Weiji Xie, Jinrui Han, Jiyuan Shi, Jiakun Zheng, Huanyu Li, Xinzhe Liu, Weinan Zhang, Chenjia Bai, and Xuelong Li. KungfuBol2: Learning versatile motion skills for humanoid whole-body control. Sep 2025. Under review.
[48] Tairan He. Jiawei Gao, Wenli Xiao, Yuanhang Zhang, Zi Wang, Jiashun Wang. Zhengyi Luo, Guanqi He, Nikhil Sobanbabu, Chaoyi Pan, Zeji Yi, Guannan Qu, Kris Kitani, Jessica K. Hodgins, Linxi Fan, Yuke Zhu, Changliu Liu, and Guanya Shi. ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA,, June 2025. doi: 10. 10.15507/SSS.000 w02.SSSS..22.......666
[49] Minghuan Liu, Zixuan Chen, Xuxin Cheng, Yandong Ji, Rizhao Qiu, Ruihan Yang. and Xiaolong Wang. Visual bodle-body control for legged noco-manipulation CoRL, 2024.
[50] Xuxin Cheng, Jialong Li, Shiqi Yang, Ge Yang, and Xiaolong Wang- Open-television: Teleoperation with immersive active visual feedback. In 8th Annual Conference on Robot Learning, 2024.
[51] Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, and Xuelong Li. Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning. Advances in Neural Information Processing Systems, 36:64896-64917, 2023.
[52] Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control scale. Robotics: Science and Systems XIX, 2023.
13

---

[53] Dibya Ghosh, Homer Rich Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, al. Octo: An open-source generalist robot policy. In Robotics: Science and Systems, 2024.
[54] Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafacl Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, et al. Openvla: An ooun-s vision- -anguaee action model. In 8th Annual Conference on Robot Learning.
[55] Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-Ib: a diffusion foundation model for bimanual manipulation. In The Thirteenth International Conference on Learning Representations.
[56] Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Robert Equi, Chelsea Finn. Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri. Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, Laura Smith, James Tanner, Quan Vuong. Anna Walling. Haohuan Wang, and Ury Zhilinsky. : A Vision-Language Action Flow Model for General Robot Control. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025, doi: 10. 10.15607/RSS 202...5X...22...X.1.
[57] AgiBot-World Contri-Conssisutooss Agihot world colossco: A large-scale manipulation platform for scalable and intelligent embodied systems, 2025.
[58] Yunfan Jiang, Ruohan Zhang, Josiah Wong, Chen Wang. Yanjie Ze, Hang Yin, Cem Gokmen, Shuran Song, Jiajun Wu, and Li Fei-Fei. Behavior robot suite: Streamlining real-world whole-body manipulation for everyday household activities. arXiv preprint wxxiv:2503.056553 2025.
[59] Hao-Shu Fang. Hongjee Fang. Zhenyu Tang, Jirong Liu. Chenxi Wang. Junbo Wang, Haoyi Zhu, and Cewu Lu. Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot, In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 653-660. IEEF, 2024.
[60] Homer Rich Walke, Kevin Black, Tony Z Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, et al. Bridgedata v2: A dataset for robot learning scale. Conference on Robot Learning, pages 1723-1736. PMLR, 2023.
[61] Kun Wu, Chengkai Hou, Jiaming Liu, Zhengping Che, Xiaozhu Ju, Zhuqin Yang, Meng Li, Yinuo Zhao, Zhiyuan Xu, Guang Yang. Shichao Fan, Xinhua Wang. Fei Liao, Zhen Zhao, Guangyu Li, Zhao Jin, Lecheng Wang, Jilei Mao. Ning Liu, Pei Ren. Qiang Zhang, Yaoxu Lyu. Mengzhen Liu, He Jingyang. Yulin Luo. Zeyu Gao, Chenxuan Li, Chenyang Gu, Yankai Fu, Di Wu, Xingyu Wang. Sixiang Chen, Zhenyu Wang. Pengju An. Siyuan Qian, Shanghang Zhang, and Jian Tang. RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation. In Proceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025. doi: 10.
[62] Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 6892-6903. IEEE, 2024.
[63] Zhi Jing, Siyuan Yang, Jicong Ao. Ting Xiao, Yugang Jiang, and Chenjia Bai. Humanoidgen: Data generation for bimanual dexterous manipulation via Ilm reasoning. Neural Information Processing Systems (NeurIPS), 2025. URL [ttpss/t:///axx org/ ///////////// 20a//2500 /2////007700333
[64] Carnegie Mellon University. CMU MoCap Dataset, 2024. URL http::/mocapp: cs. cmu. edu
[65] Lloyd S Shapley. Stochastic games. Proceedings of the national academy of sciences, 39(10): 1095 1100. 1953.
[66] Richard S Sutton, David MeAster,er, Satinder Singh, and Yishay Mansour. Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems, 12, 1999.
[67] Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng. Training gans with optimism. In International Conference on Learning Representations, 2018
[68] Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras. Cycles in adversarial regularized learning. In Proceedings of the twenty-ninth annual ACM-SIAM symposium on discrete algorithms, pages 2703 27 17. SLAM, 2018
14

---

[69] Martin Heusel. Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017.
[70] Tianyi Lin, Chi Jin, and Michael Jordan. On gradient descent ascent for nonconvex concvveeeeeeeeeeeeeeeeeeeeeeeeee minimax problems. International Conference on Machine Learning, pages 6083-6093, PMLR, 2020.
[71] Constantinos Daskalakis, Dylan Foster, and Noah Golowich. Independent policy gradient methods for competitive reinforcement learning. Advances Neural Information Processing Systems, 33:5527 5540, 2020.
[72] John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International Conference on Machine Learning, pages 1889-1897. PMLR, 2015.
[73] Bhairav Mehta, Manfred Diaz. Florian Golemo, Christopher Pal, Liam Paull. Active domain randomization. Proceedings of the Conference on Robot Learning, volume 100, pages 1162-1176. PMLR, 30 Oct-01 Nov 2020.
[74] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference Machine Learning, pages 8748 8763, 2021.
[75] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning, Advances in neural information processing systems, 30, 2017.
[76] Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu. Xi Shen, and Ying Shan. Generating human motion from textual descriptions with discrete representations. In IEEE/CVF conference on computer vision and pattern recognition, pages 14730-14744 2023.
[77] Bryan Parno, Jon Howell, Craig Gentry, and Mariana Raykova. Pinocchio: Nearly practical verifiable computation. Communications of the ACM, 59(2):103 112, 2016.
[78] Yuzhe Qin. Wei Yang. Binghao Huang, Karl Van Wyk. Hao Su, Xiaolong Wang. Yu-Wet Chao, and Dieter dexterous robot arm-hand teleoperation system. Robotics;
Fox. Anyteleop: A general Science and Systems, 2023.
15

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A Theoretical Analysis
In this section, we provide more theoretical analysis for the Markov game in our method. We give the analysis of the zero-sum game learn the locomotion policy n', and similar analysis can be derived for learning x".
Recall that in learning x². consider the lower body agent, and the upper body is adversary that causes adversarial disturbances to the locomotion policy. Thus, the lower-body policy receives the command- followiingg reward as r' (s, at, ",,"", ,,"",, and the upper-body policy obtains a negative reward- -r' -r' a', aa). Formally, the value function V (z',, x²)) is defined
('ss
En ('(st, a' a²) ,a")\so =s]
(8)
Then we have the value function as V3(''', x²) = Eaup! Bamp/V x', , =²'], which is defined by the expectation of accumulated locomotion reward r', According to the theory of stochastic game [65], for any game G. there exists a Nash equilibrium (Tij mz) such that (, T2) S (T Tÿ) S (M v5). Then we have
Va( x²*)= S V x =² S Vo((='' " x"), for ( x',=".
(9)
and in particular
V = ( xx" max min V x x²) min max Va x²),
(10)
which signifies that n' is Iearned to maximize the value function to better follow commands in locomotion, while x² tries minimize the value function, aiming to provide an effective disturbance to help Ieam robust locomotion policy. Our goal in this setting is develop algorithms find e-approximate Nash equilibrium, i.e. to find x' such that
min Va x²) - (TT' T "T" () S
(11)
To solve this min-max problem in Eq. 10 we adopt independent RL optimization process for both players. Specifically, the agent obtains [[00, un: ro....: (ST, a'r, ( r.)], and adversarial obtains [(s0. ab)r") ...")................... (ST, at. r4]] by executing each policy the game to sample a trajectory, where each player is oblivious to the actions of the other player. In our analysis, we adopt continuous policy parameterizations x + n². and + x², where x E X C Rdd,, E )' C Rd2 are parameter vectors. Each player simply treats (2, y) + (m"", T²' continuous optimization objective, and updates their policy using REINFORCE gradient estimator (66). For episode i, the two players update their policies with stochastic gradient descent, as
(0+1)) Px(x') na (
y (++)) <- Py(y
7/u (1)).
(12)
where Px denotes euclidean projection onto the convex set X. with
log x (a, (aa())
v log x' (a 1(()) (t)))
(13)
where R2)5) + Ei Tt This process is independent for two players, as they optimize policies independently with policy gradients using their own experiences. Here, is well-known that players update their policies indopendently using online gradient descent/ascent with the same learning rate, the resulting dynamics may cycle, leading to poor guarantees 667 68). However, previous studies show that two-timess two-timescale rules help policy converge simple minimax optimization settings (69 77). As result, we follow recent studies in the Min-Max game [71] and use wwo-timexcale updates for the two players, which a simple modification the usual gradient descent-ascent scheme for Min-Max optimization, in which the Min player uses a much smaller step size than the Max player. Next, to ensure that the variance of the REINFORCE estimator remains bounded, we require that both players use e-greedy exploration in conjunction with the basic policy gradient updates.
Assumption A.1. Both players follow direct parameterization with e- e-greedy exploration, as T" ra" | s) = (1-&x) 1 a.." + AA and x' r'(a' () (1 1l + A where Ez, [0, 1] are the exploration factors.
Then the following theorem holds by following Assumption A.1 and the two-timescale rule of update.
Theorem A.2 (Restate of Theorem 3.1). Given e > O, suppose each policy has e-greedy exploration scheme with factors of Ea x e and ε. x é. under a specific two-timescale rule of the two players' learning-rate following the independent policy gradient, we have
max min Vo(T' n²) we
R
1
N Σ
min Va(T7 (nnn "
x(00)
Se
(14)
1=1
after N episodes, which results in c-approximate Nash equilibrium.
22

---

Proef. This proof basically follows Appendix A.2 of Daskalakis et al. 771], which proves that the Max player leads to the e-approximate Nash equilibrium. We can follow similar process to prove that the Min player also the same property. According to 7711. by following the REINFORCE gradient estimator, we have
1A42 E,54
14
B ml V
(=, = 21
and Eng,, 18, V, Vo v)ll 21
(15)
EyS4
where ( is the stopping probability of the Markov game. Then according the performance difference lemma 72
have
Va(TT Vo(aT" x²)
Σ
d
(1)
Arm (s.a.
(16)
Then, for policy n. let (T')) 1x'( denote a policy minimizing (5ttrl 1l,0. then have
V v) - min Va v) s Vo( - V=(Ti(=)),
(17)
x'
Σ
de p (It) s) "" (s,
(18)
d (+))) him (5) max Eiwn 1 (s,a,b)
(19)
(e)())
(5) max B (101 (s, b)]. do
(20)
d,
7 (+ )* )
S Cg. where Cg the minimax mismatch coefficient d arttttio
We observe that
4, ,
for the game G. Then we have
Σ jos
(8) max Ea (aw
4.4
Σ dS. (s) (s,
max REA(A*)IS]
Σ dp (s)(TT (a () Ia 1n (s,a,
2EA(AE)18 s.0
Σ 3 (s)((1 - 0x + + Ze ae (on (s,
max
=EA(A*) [s|
Σ E
(s) Ex)ana tEN entua - (1 - Ec) Bt 10 1Q"h nt as,
max SEA(A-)|S)
25, Σ J5.h , (5)(En( - End Bao (s, (s,a,))))
< (1 Ex) max
2EA(A
25.
max =18(
VVV. 5) +
(21)
where the last equation holds since Pr(T 2 t S for any 2 0 that for any P E A(S),
T
VzVola,y) = Po
Σ
v log (a (st,
(22)
(23)
.ES
Thus, for any & E S, a E A. we have
(V,(u.))
(1 ez))d (s) ( 6)],
Or..a
and so it follows that
(V,(2,)) 0r.
< (az:a(()) (s)
Es (1 ToiTy b)]]
23

---

According to Eq. 117 and 21). we have
min V2("T, , min Va (
1= max
(VeVo(T","") x');"" xED(A
5)
22, ç2
P
(24)
The term in the right side be bounded by O according the gradient dominance condition in Lemma of 77. Then, according to Theorem of ?T, suppose each policy has e-greedy exploration scheme with factors of Ex x and E. x ²,, the average performance difference can be bounded. Specifically, under specific two-timescale rule of the two players' learning-rate, we have
min Va( T²) af
E
N Σ
min VT( , x1))))
50
CC2 C
(25)
which concludes our proof.
B
Implementation Details
B.1 State and Action Space
this section, we introduce the detailed observation and action-space information of policies used experiments.
The adversarial iterations use the same state space setting. use 21 DoF the H1-2 robot without its wrist. joints. The details are in Table 7
Table 7: State and action space information in ALMI setting.
State Term
Lower dim.
Upper dim.
Whole dim.
Base angular velocity
3
Base gravity
Commands
3 (velocity)
(motion) 21
12 (velocity+motion)
DoF' position
21
21
DoF velocity
21 12 (lower)
21 9 (upper)
21 21 (whole)
Actions
Periodic phase
Total dim
65
68
83
The policies use the PPO I algorithm for training, where the ohservation of the critic policy has 3 additional dimensions of base linear velocity compared to the state space of the actor policy, which is often called privileged information in robotics. For action space, the lower body policy uses 12 DoF of two leg joints, the upper body uses DoF one waist joint and two arms, and the whole body policy uses all 21 DoF joints.
B.2 Training Detail of the Locomotion Policy
Algorithm Description We employ PPO to train the locomotion policy, which consists of two key compo- nents: the policy and the environment. Trajectories generated by deploying the policy on the robot within the environment, and the collected data is then used update the policy. Additionally, the curriculum factors used to adjust at the end each episode. The algorithmic description given Algorithm D
Implementation Details To enable the robot exhibit a regular gait during movement and remain stationary when the velocity command is zero, we design gait phase parameter Φ. (drt.tess &r.ngu)) as an observation term, which is updated as:
01+1,en dr.tet f dt,
(26)
@c+1,right 1c+1.leh + vb
(27)
where f. dt, di are gait frequency, time step and gait offset, respectively. We use PPO to train the policy and employ asymmetric actor-critic architecture, where the critic is granted access to the base linear velocity o, as privileged information. For the first iteration, apply an open-loop controller to control the upper-body track the reference motion trajectory. subsequent iterations, we employ trained upper-boot policy as the upper-bod controller.
Reward Design We divide the reward terms into three parts according their role: penalty rewards to prevent unwanted behaviors, regularization to refine motion, and task reward to achieve goal tracking (velocity commands or upper body DoF position). The details are in Table The three iterations use the same reward terms and weights.
24

---

Algorithm Training process of the locomotion policy
Require: max iterations N, max episode length E
I: Initialize policy ne. value function V/
2: for iteration = ,,...,N .N do
3: Collect trajectories by running policy n.J in the environment Update curriculum episode termination
5: if (T > 0.8 x mux then
6: ad 4 min Oa + m, || - w)
7: else
8:
ag max 2w,
end if
10:
if oa == M| w then
11:
min + 0.05, 1), oz <-
12: else as == 0 then
13: a₂ max(o. 0.01,0) 0)
14: end if
15: Perform PPO update with GAE advantage update
16: end for
17:
return Trained policy π
B.3 Training Detail of the Motion-Tracking Tracking Policy
Algorithm Description During the training of the upper-body motion tracking policy, the pre-trained lower-body policy n' to execute the locomotion command and simultaneously introduce disturbance to the upper-body. We begin by sampling the locomotion velocity commands from narrow range and compute the tracking error of the motion-tracking task. each episode termination, if the tracking error is smaller than a threshold, the command sampling range will extended to generate more intensive disturbance to the upper-body. We use PPO to train the motion tracking policy to simply follow the reference DoF position sampled from the dataset. We did not use the 6D pose of keypoints as an observation because the computation is based on the robot's root pose, which will differ from that of motion dataset under omnidirectional commands. In contrast, joint angles avoid such dependency and provide sufficient information for motion tracking.
Curriculum Setting In learning the motion tracking policy in the upper body, the lower-body command follows curriculum by adjusting the range of the velocity commands. Formally,
max Ciui 0.1, Cmin),
exp( 0 q" (1)) d'
Cain
max Cuuin + 0.1, Clmn),
otherwise
(28)
min Clnxx + 0.1, C Ci min(c 0.1, C,
exp(-0.51lq" 4" > d"
(29)
otherwise
where ciuin: cjnx are the lower and upper hound of the current command range, el is sampled from[c c during training. [Cinns Clax] is the range limit of pre-defined commands. qº, q" and d" are the reference upper body joint position, upper body joint position and the threshold of motion tracking error, respectively. The related terms and values are given Table
Table 8: Upper body curriculum terms and values.
Term
Value
C C
[-0.7, -055. -0.5] [0.7, 0.5, 0.5]
d
0.9
Reward Design Table 2 gives the reward terms for the motion tracking policy in the upper body. The upper-body policies of adversarial iterations all use these reward terms and weights.
B.4 PPO Hyperparameters
We use GAE to estimate the advantage and CLIP-PPO to train our policy. The total loss is given by:
Liotal
Lpolicy Wy Lvalue W.S.
(30)
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where
Loolley (r: clip((: (0) c)A.)
(31)
Lvvaue [[(V(a,, R,)
(32)
E[- E[- log (a]s)]]
(33)
where (0) =,(n,]+,) seur (ox [m:] and A, the estimated advantage. we and w, are value loss coefficient and entropy
term coefficient, respectively. The hyperparameterss of the PPO algorithm and the information of the network
backbone are listed in Table 10
B.5 Domain Randomization
Domain randomization is popular technique for improving domain transfer, often used in zero-shot setting when the target domain is unknown cannot casily be used for training 773] In order to adapt the trained policy the real world, employ the domain randomization technique during training facilitate robust sim-to-sim sim-to-real transfer [3 37.. We give the domain randomization terms ranges used during the training process, and the details are in Table
Table 9: Domain randomization terms and ranges.
Table 10: Hyperparameters related to PPO
Term
Value
Hyperparameter
Default Value
Dynamics Randomization
Actor Istm size
[64]
Actor MLP size
[64, 32
Friction
24 1.25) 24( 3, 5) kg
Critic MLP size
[64. 32]
Base mass
Optimizer
Adam
Link mass
((099, 1.1) default kg
Batch size
4096
Basc CoM
24( (0(-0.1..00 0.1) 24(0, 40) ms
Mini Batches
4
Control delay
Learning epoches
Activation
elu
External Perturbation
Entropy coef( w.))
0.01
Push robot
interval 10s, Uyy Im/s Randomized Terrin
Value loss cocf(w,,)
1.0
Clip param Max grad norm
0.2
1.0
Init noise std
0.8
Terrii type
trimesh, level from to 10
Leamiing rate
1c-3
Velocity Command
Desired KL
0.01
GAE decay factor( X)
0.95
Linear velucity
1.0, 1.0)
GAE discount factor(
0.998
Linear velocity
24.-0,30.33 Angular yaw velocity
Curriculum window size(w:
40
((,0.5,,0.0
C Data Collection and Model Details
Dataset Overview
The ALMI-X dataset contains 1989 motions combined with 41 commands, resulting 81,549 trajectories totally. Each trajectory is organized as [Tí -á, -dof_ Téraus Trot I respectively representing robot states, actions, joint angles, global position and global orientation, where the global position and global orientation are usually considered as privilege information that is missing in the state space. The statistical results shown in Fig. 4 Fig. [aland Fig. [6]emonsttrae the advantages of ALMI-X, XX and the experimental results of Appendix D further indicate that dataset is higher quality and suitable for foundation model training. The dataset is available at //ttts::/aaaaa hwa.iii-mmaoiddd gi githuu.... iio/
Data Collection Details (i) For lower-body, we first categorize command directions into 11 types (8 transla- tion directions: front, back, lickk,ed left, right, front-left. front-right, back-left, and back-right; 2 rotation directions: left and right; and keep standing) according to different combina- tion of linear and angular velocity command, and define 4 difficulty levels for command magnitudes, as shown in Table II] We sample velocity value for the first 3 levels and task fix value for the 'fix' level, which corresponds
Table 11: Command difficulty level setting.
Command
Level
6x.t
0y.t
Wyow.
easy
[0.2.0.4]
[0.2.0.3]
[0.2,0.3]
medium
[0.4.0.5]
[0.3.0.4]
[0.3,0.4]
hard
[0.5.0.7]
[0.4.0.5]
[0.4.0.5]
0.4
a text that does not contain speed-relate reeeted modifiers, e.g. just 'go forward' without 'slowly" 'fast'. We take the lower-body commands according to the difficulty levels with corresponding ranges, combining with different direction types. In addition. we set special category for standing, resulting in 41 categories totally. (ii) For the upper body, select motions according to 44 and remove those that overly large in amplitude difficult
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to distinguish, resulting in 680 different motions. However, these motions have uneven distribution. with each motion category associated with different number of trajectories. As shown the left plot of Fig. for instance, the number of steps for "wave" greatly exceeds that of "salute". Through empirical evaluation, find that imbalanced data distribution negatively impacts the training performance of the foundation model.
To address this problem, we classify the motions into 30 categories based on their corresponding texts. Then we count the number of steps in cach category and augment those with fewer steps by repeating their motion sequences multiple times during data collection. This is not equivalent to mere data duplication, since environ- mental interactions same reference motion. After that, data volume across all categories approximately the same, as shown Fig.
the robot produce slightly different trajectories each time, despite following the
After data augmentation, we obtain total 1,989 motions. When combined with different direction and difficulty commands, these motions generate a total of 81,549 trajectory sequences. Fig. S shows the planar position of all steps in space. The visualization result shows that despite the accumulated errors from the policy and the limitations of the simulator, the robot is still able execute velocity commands in reasonably correct manner. instance, under the 'go forward' command, the steps are distributed along the positive x-axis, cnabling the foundation model to learn representations across the space.
Fig. 6 iiillsttatts the distribution of the upper body-hand positions of datasct. The visualization shows that the upper body motions in the dataset basically allow the robot arms cover the entire activity space. When we train the lower body policy, range of two arms from default position to whole activity space.
the curriculum which is detailed in $3.2 to gradually expand the activity
Comparison to Humanoid-X [13] data Compared to the existing texi-to-action dataset, Humanoid-X [13. dataset exhibits much higher quality. which can be utilized for relatively stable open-loop control of the robot simulations without causing the robot to lose control. to track motions have complex sources and can introduce dangerous or unreasonable behavior. However, due to the robust lower-bode policy and expressive upper- upper-body policy obtained through adversarial and curriculum learning, our collected ALMI-X dataset exhibits high exccutability. This attribute benefits the learning of the robot foundation model via supervised learning paradigm, since the learned model would be better for deployment and also ensures safety the robot.
because the RL policy that UH-I employs
D Experiment Results of Foundation Model
In this section, introduce the architecture details and inference process of the Transformer-based foundation model. Then, we give experimental results analysis trained model.
Architecture Details During the training process, we first extract segment of states and actions with fixed length И and its corresponding language description T, then concatenate the state and action for the frame resulting in new sequence {(*L, a.), (S1+1, a.+1), .... (8i+ Si+HH110 (1+ each of which is projected onto an embedding vector 2; = Linear(O,, a.). ). use CLIP 74) and linear layer process the language description T into vector of the same dimension Ttext Linear (CLIP (T)) These vectors serve as input to the transformer decoder, which predicts the subsequent actions corresponding to each input. The computation process of the (-th transformer decoder layer and the output layer be represented follows:
Linnt b .. 2.4 CausalSA(zivs) 1-1 xi- ... Di #148-17
(34)
fa.t,... d.#H) Linear xi xitH-1)
N
(35)
wwwe
open krm
pour and mix
tabe bishitt
stratch out
Laka book from shelf
violin
play golr
expand
gultar
wipe
make how with hand cup
punch
151,262 steps
drink
shake
cut
cast
wash
Nigh five make the sign
drow
Figure 4: Percentage of steps for different categories of motions before and after data augmentation.
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go forward
go left
go backward
Y axiis (m)
go right
go forward the right
go forward to the left
go backward to the left
go backward to the right
turn left
turn right
keep standing
7.5
-5.0
-2.5
0.0
2.5
5.0
7.5
X axis (m)
Figure 5: The visualization of x coordinates of the robot for each step in the dataset. We nown-sample the data for visualization.
where N is the number of transformer decoder layers, āz. where i i Siiiiiii 1, represent the predict action integrating command information and historical state-action pairs. Mean square errors between predict and ground truth actions are used for policy update.
Model Inference inference, the input text vector frext remains constant throughout the exccution of task. The robot executes the action decoded from the last vectors of the policy output. It then concatenated with the newly acquired state. which is then computed into embedding vector and serve as the last vector of the input for the policy predict the action. In the initial stage, when the length & of the historical than H, the robot execute action decoded using vector corresponding to the latest state-action pair rather than the last one.
state-action pairs
Implementation Details We investigated multiple designs for traguage-guiderr whole-body humanoid control through supervised learning using the ALMI-X dataset. We evaluate the Transformer architecture with different input sequence lengths for the closed-loop robot control approach, while also exploring the open-loop control approach prediction and exccution of the entire sequence with tokenizer like UH-1 13. Specifically, the Transformer is trained with different state-action sequence lengths. We selected 20 and 400 as the input sequence lengths for the closed-loop control, while the open-loor spen-loop control approach predicts the entire motion sequence with maximum sequence length of 700 (175 after tokenization). For shorter sequence, expect short-term historical information, allowing
the model focus for longer sequence, we aim to integrate information from complete motion sequences modeling, thereby better responding to text commands. For tokenization, we employ the same VQ- VAE (7 architecture as in
robust locomotion performance. As
Figure 6: We illustrate the hand positions in the dataset relative to the robot's hand joints. From left to right, scaling factors of 0.05, 0.5, and 1.0 are applied to the arm joint angle offsets from the default pose, effectively modulating the motion amplitude. Larger scaling results more pronounced arm movements.
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Table 13: Survival duration (SD) and success rates of upper-body(S (S Rup Mlower-body(S Rlow) with different text commands.
Table 12: Average velocity of Foundation models with different text commands.
Average Velocity
Metrics
Text commands
Text commands
S R
S
SD
w.r.t lower body
Ex
By
Gyyw
CL.22sll
C1-20sl
go forward slowly
forward fast
0.48
0.18
0,08
and wave left.
1.00
0.20
8.0
left fäst
0.07
0.28
0.00
and wave right. right fäst and wave both.
backward moderately
1.00
0.20
hackward
left slowly
-0.19
0.21
0.00
1.00
0.40
8.0
turn left slowly
0.02
0.02
0.08
CL-400s1
go right fast
-0.11
-0.37
-0.07
go forward slowly and left. go backward moderately and wave right. right fast and wave both.
keep standing
0.00
0.00
0,01
1.00
1.00
2.14
Cl..40ol
1.00
1.00
2.54
forward fast fast
0.87
-0.05
-0.10
0.00
1.00
3.57
0.53
0.42
-0.07
hackward to
left slowly
0.35
0.30
-0.01
OL.
turn left slowly
0.53
0.16
0.2
and wave left. go backward moderately and wave right. go right fast and wave both. 0.00
forward slowly
0.00
1.00
0.31
go right
0.47
-0.50
-0.10
keep standing
0.22
0.08
0.00
0.00
1.00
0.29
1.00
T2M-GPT [76] to tokenize four state-action pairs into one token; while for the non-tokenized design, we utilize linear layer project both the text command feature and stale-action pairs into shared latent space.
Evaluation Metries Since training foundation model follow all text commands in ALMI-X is quite challenging, we conduct preliminary investigations using only wave-relative motion. We evaluate the models using different control approaches and input sequence lengths with different text commands in MuJoCo simulations, repeating each command 5 times with each test lasting 8 seconds. we adopt the following notation: CI. - æsl denotes the closed-loop control approach without tokenization, where represents the input sequence length (e-g., CT. 20sl). OL indicates the open-loop control approach with tokenization, which predicts and executes the entire motion sequence in a single forward pass. We evaluated several metrics, including robot survival time (up to 8 seconds), velocity during test time, and success rate of lower-body and body. We consider the lower-body body successful if the robot's 's movement direction matches the text command, and the upper-body successful if the robot waves the correct hand.
Experiment Results Results in Table 13 demonstrate that the open-loop control approach, which predicts and executes the entire action sequence based single command, fails maintain the robot's balance
switch control mode
Deployment
Medion Datneal
Inverse kinematics
retarget
upper body joint position
Proprioception
lower body joint position
Figure 7: Real word deployment overview. The deployment framework contains Apple Vision Pro, router, a Unitree HI-2 robot and PC which runs our policy and telcoperation server. The boder-body target DoF pos is given by teleoperation or motions from our motion dataset. We can switch the control mode via the remote controller. Through this framework, the robot can perform upper-body motion during movement, and diverse doco-manipulation tasks.
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and leads to an extremely low survival time. In contrast. the closed-loop control method exhibits more robust locomotion and superior lower-body rext-following performance. Table demonstrates that the closed-loop approach with short input sequence length enables robot's movement direction adapts to commands such as forward and right and its velocity adjusts to modifiers like fost and slowly, aligning more accurately with the sampled velocity commands as shown in [I] However, the approach with longer sequence length results in poorer towe-bodd motion performance in both movement dircction and velocity. It can be inferred that the long input sequence length plays a crucial role in the upper-hody success rate, since OL, and CL 400sl achieve much better upper-hody compliance with text commands, and focusing the model exclusively on short-term historical information brings locomotion stability and better command performance of the lower body. These experimental results indicate that training a humanoid foundation control model using supervised learning and a large-scat high-que high-quality dataset represents feasible approach with substantial exploratory potential. However, we find that the current model faces challenges when extending the training data to the entire ALMI-X, indicating limitations of the current model in handling highly diverse behaviors.
E More Experiments Regarding Adversarial Training
We also conducted more experiments to verify the rationality and advantages of the simplification to the original method.
Without simplifying the max-min problem, four separate policies would be required in each round of policy learning. Specifically, in round 1, when updating, an additional adversary is required; when updating. an additional adversary is required. It would be computationally expensive to train an additional adversary in each round. As result, we change the inner-loop optimization of the adversary from the parameter space the command space, which allows us to sample adversarial command to approximate the effects of training adversarial policies. We add additional experiments to show the necessity of the simplification:
we do not adopt the simplified approach, we would instead directly train an adversary to attack the policy. However, it is very challenging to properly control the strength of the adversary. example, we jointly train upper-body adversary to minimize control adversary strength, where oction. __iip__ppppp
Train an Adversary.
Take the training of lower body policy the lower-body reward. We vary action clip_ 100 x action clip scale. The results are shown in the Table [4] It can be seen that large adversary collapse training, while weak adversaries yicld poor robustness. In contrast, our curriculum based method, guided by motion difficulty, provides effective and stable adversarial training.
Table 14: Comparison before and after simplification of the adversarial training.
Metries
Method
Evel 4
Eang 4
guspper Jpc
4
gupper lape
pispper action
gllower 4
Eg 4
Survical 1
MLMI (Ours)
0.2202
0.4812
0,2116
0.8458
0.0600
0.0175
0.8511
0.9723
action elip, scale 0.1
1.2610
5.3455
6.4459
1.7446
catioo_clii_scat 0.01
L3307
6.8632
6,1924
2.3056
Policy with adversarial goals. Another way implement adversarial training is to assign each policy two objectives. For this experiment, we train upper and lower policies jointly, each maximizing its own reward while minimizing the other's. s. vary adversarial strength via reward weights (c.g.,-1 -1 for strong adversary and 10.1 for weak adversary). Table [5]low adversarial weight greatly affects training, and tuning it is nontrivial. The training of weak n.. strong wi, and strong ~. + strong dh failed. Our simplified method ensures stable and effective adversarial interaction. Among converged settings, our method outperforms others in all metrics.
Table 15: The lower and upper policies are updated simultaneously, and both have two sets of goals.
Metries
Method
Eye1 4
Eang
guppper
pupperr
Action
E ucciion
J
Eig 4
Survival f
ALMI (Ours)
0.2202
0,4812
0,2116
0.0458
0.0600
0.0175
0,8551
0.9723
weak = wesk af strong
0.3415
1,4413
0.29269
0U0526
0.9900
0.9612 0.0133
1,0200
wesk m
1,1230
4.6715
2,7251
0.1840
0.3450)
4.6937
1,702
0.3524
F Real-Robot Deployment
Hardware and Deployment We conduct experiments using the Unitree H1-2 robot equipped with the ROBOTERA XHAND robotic hand, applying ALMI-trained policies to the lower body control and various interfaces (i.e., open-loop controller, ALMI policy, and VR device) for the upper body control, shown in
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(a) Salute
(b) High five
(c) Swing tennis racket
Figure 8: Upper body behavior of the robot during locomotion compared between simulator and real world. simulator, robot keeps standing to show the original upper body motion; In real world, robot goes forward while doing motion.
Fig. 7 Specifically, (i) we can use an open-loop controller that sends the target joint positions of the pre-loaded motions; (ii) we can obtain actions of the upper hody via the policy T", which uses the reference joint positions of the preloaded motion as input. In real-world experiments, accuracy when the robot remains standing, hut the learned policy shows better stability during movement. teleoperation systems, which enable the robot to perform loco-manipulation
find that open-loop control has higher tracking
(iii) The other one combining tasks. Specifically, the upper-body follows wrist poses of humans and the relative positions between the robot end-effectors and the robot head are expected to match those between the human wrists and head, then the arm actions are obtained from inverse kinematies. The finger movements control the dexterous hands loco-manii manipulation tasks. Benefiting from the superiority of the training method and the use domain randomization, our policy does not require additional sim-to-rea techniques and can be deployed directly on the NVIDIA Jetson Orin NX onboard the robot for inference. The action output frequency of the policy is 50Hz.
also captured by VR simultancously to
Teleoperation for Loco-Manipulation. Due the absence of hand keypoints in the motion dataset, we use the VR device to capture human hand poses. This was then used real- cal-time control robot's dexterous hand, enabling to perform various Inco-manipulation tasks. In this setup, the robot's camera provides real-time, first-person 3D visual feedback to the VR device, allowing the operator to see through the robot's 's perspective. The system translates human wrist poses into the robot's coordinate frame, ensuring that the relative positions between the robot's end-effectors and head mirror those of the human wrists and head. The robot wrist orientations are calibrated match the absolute orientations of the human wrists, as determined during the initialization of the hand-tracking system. We employ closed-loop inverse kinematics. implemented with Pinocchio (77). to calculate the robot arm's 's joint angles. The human hand keypoints are translated into robot joint angle commands through dex-retargeting, utilizing flexible and efficient motion retargeting library (78 Our method further leverages vector optimizers to enhance the performance of dexterous hand control. The teleoperation system basically follows Open-Tedn Television
Real-robot Experiments on Motion Tracking In the real world, we use the remote controller to control the robot to complete a series of motion tracking tasks. Fig. provides some illustrative examples of our ALMI's motion tracking quality compared between simulation and real world. These results demonstrate that policy
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achieves comparable performance in tracking speed commands and upper-body motions in the real world as in the simulator, without relying on any additional sim-to-real techniques.
Loco-Manipulation By leveraging the ALMI policy and teleoperation deployment, we enable the robot accomplish variety of real-world loco-manipulation tasks. Fig. Dilluttrates that the robot can hit the tennis ball with a precise swing. Our robot can also complete houschold tasks, which requires a combination of stable movement with manipulation capabilities. In Fig. [T]a), the robot can carry Skg box and walk stably; while in Fig. [0]b), the robot can use vacuum to clean the floor.
Real-robot Experiments
Figure 9; Robot swings the tennis racket with precision.
(a) Carry box
(b) Vacuum floor
Figure 10: Robot performs loco-manipulation tasks with Teleoperation. (a) Carry 5kg box. (b) Vaccum floor.
G Broader Social Impact
Our humanoid locomotion method with teleoperation enables robots perform diverse tasks in human vironments, with potential applications disaster response, healthcare, and industrial automation. However. challenges such as job displacement and safety risks must be addressed to ensure responsible deployment. Ethical guidelines and policy discussions will be crucial to maximize societal benefits while minimizing unintended consequences.
32


Reviews:
[{"review_id": "fa0h2Qs5cL", "reviewer": "Reviewer_RhvN", "summary": "This paper presents the ALMI framework, which introduces an adversarial learning paradigm for training upper and lower-body policies in humanoid control. The key idea is to formulate the coordination between upper and lower body as a two-player zero-sum game, thereby encouraging robustness and precision in full-body motion generation. The method is validated through comprehensive experiments both in simulation and on the real H1 humanoid robot. Additionally, the authors contribute a large-scale language-annotated dataset for whole-body control, which holds promise for advancing foundation policy learning in robotics.", "questions": "1. In Lines 195–196, the adversarial signal to the upper-body policy consists solely of velocity commands from the lower body. Could the authors clarify why this information alone is sufficient? For instance, would incorporating additional signals such as gait type, gait frequency, or phase provide stronger adversarial influence and improve learning outcomes?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n1. Novel adversarial learning design: The proposed adversarial formulation for upper-lower body coordination is a promising and innovative direction. Framing the interaction as a two-player zero-sum game, combined with curriculum learning for the lower body, introduces a structured and scalable approach to whole-body policy learning.\n2. Real-world deployment: The evaluation on the H1 humanoid robot in real-world settings is a major strength, demonstrating the practicality and robustness of the proposed method beyond simulation.\n3. Good writing and experimental design: The experiments are well-structured, with evaluation metrics covering motion tracking accuracy, command fidelity, stability, and survival rate. The ablation studies effectively highlight the benefits of different modules within the framework.\n4. Open-source contributions: The release of code and a large-scale dataset with language annotations adds substantial value to the community and paves the way for future research in language-conditioned whole-body control.\n\n## Weaknesses\n1. Reward design in Appendix: The reward structure, which plays a critical role in training, is relegated to the appendix. Including a concise version of it in the main text would greatly benefit the reader's understanding of the method.\n2. Symbol clarity: The preorder symbol introduced in Line 117 should be clearly defined to avoid ambiguity.\n3. Limited baseline comparison: The experiments compare only against Exbody. Including additional baseline methods, such as recent approaches in modular or hierarchical control, would strengthen the empirical validation.\n4. Minor typos:\n   - Line 90: The symbol should be \\( a^u \\) instead of \\( a_l \\).\n   - Line 159: The citation should refer to Eq. (1) instead of Eq. (2).", "was_revised": true, "final_justification": "I maintained my score of 5, appreciating the contribution of the work.", "rebuttal": "We sincerely thank the reviewer for the positive assessment of our work.\n\n**W1: Reward design in Appendix**\n\nThank you for the suggestion. We will consider moving important components such as the reward design and curriculum settings to the main text to aid readers' understanding.\n\n**W2: Symbol clarity about the preorder symbol introduced in Line 117**\n\nThanks for the suggestion, we will add the definition of the preorder symbol in the paper. The symbol $a\\asymp b$ means two variables have the same order of magnitude. Specifically, there exist positive constants $C_1$ and $C_2$ such that for sufficiently large values of the relevant variable, we have $C_1 \\cdot a \\leq b \\leq C_2 \\cdot a$ holds. \n\n**W3: Limited baseline comparison**\n\nThank you for the suggestion. To indicate that our method can be extended to other robot platforms, we added new baseline methods: OmniH2O [1], Exbody2 [2] and compared them with our method on Unitree G1 robot.\n\n- **Experiment Design and Evaluation Metrics.** We adopt the CMU MoCap dataset with $1122$ motion clips to evaluate different metrics, which are same as our baseline experiment in paper, in IsaacGym. As OmniH2O and Exbody2 are imitation-based whole-body control methods without velocity tracking, so, for ALMI, we used linear and angular velocity provided by motion dataset as velocity tracking command; for the other methods, they directly track motions.  \n\n- **Training Details and Checkpoint Selection.** All the policies are trained on one NVIDIA RTX4090. The training time and the checkpoint of each method selected in the baseline experiment are shown in the following table:\n\n\t|             | Training Time Consumption                                    | Experiment Checkpoint             |\n\t| ----------- | ------------------------------------------------------------ | --------------------------------- |\n\t| ALMI (ours) | Lower-1～3: 10k, 8k, 6k iterations with 2.5s/iter; Upper-1～2: 3k, 3k iterations with 2.0s/iter. **Total: about 20.0 hours** | Lower-3 6k iter + Upper-2 3k iter |\n\t| OmniH2O     | Teacher: 30k iterations with 1.5s/iter; Student: 10k iterations with 1.5s/iter; **Total: about 16.6 hours** | Student 10k iter                  |\n\t| Exbody2     | Teacher: 20k iterations with 2.5s/iter; Student: 4k iterations with 2.0s/iter; **Total: about 16.1 hours** | Student 4k iter                   |\n\n\n - The comparative results are shown as follows. The experimental results show that: 1) Our method has excellent scalability for the robot platform. 2) Compared with the current popular imitation-based whole-body control methods, our iterative adversarial upper and lower body method is still promising in tracking accuracy and robustness.\n\n|Method|$E_\\text{vel}\\downarrow$| $E_\\text{ang}\\downarrow$| $E_\\text{jpe}^\\text{upper}\\downarrow$| $E_\\text{kpe}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|ALMI (Ours)|**0.1396**|**0.2776**|**0.2367**|**0.0411**| **0.0198**|**0.7411**|**0.0977**|**0.9484**|\n|OmniH2O|0.1615|0.4166|1.0826|0.0598|1.2773|2.3219| 0.1696|0.3882|\n|Exbody2|0.4015|0.6066|0.3821|0.0719|1.1797|1.3547| 0.3367|0.8848|\n\n[1] He, Tairan, et al. \"Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning.\" arXiv preprint arXiv:2406.08858.  \n[2] Ji, Mazeyu, et al. \"Exbody2: Advanced expressive humanoid whole-body control.\" arXiv preprint arXiv:2412.13196.\n\n**W4: Minor typos**\n\nThank you for the typo corrections, we will fix them.\n\n**Q1: Additional lower body signals for stronger adversarial influence**\n\nThat is an insightful question. We agree that incorporating richer gait-related signals could strengthen the adversarial influence. However, the adversarial signal depends on the input command and capability of the lower-body controller. Since our main target is mobile manipulation task, we did not consider diverse gait types for lower body. We plan to explore this direction in future work, potentially by integrating with frameworks like HugWBC [1].\n\n[1] Xue, Y.; Dong, W.; Liu, M.; Zhang, W.; and Pang, J. 2025. A Unified and General Humanoid Whole-Body Controller for Fine-Grained Locomotion. In Robotics: Science and Systems (RSS).\n\n---\nWe hope these clarifications have fully addressed your concerns. If you have any further suggestions, please feel free to reach out."}, {"review_id": "P6qzNowLeV", "reviewer": "Reviewer_F1ym", "summary": "This paper introduces ALMI (Adversarial Locomotion and Motion Imitation), a novel framework for humanoid robot control that addresses the challenge of whole-body coordination by separately learning policies for upper and lower body control through adversarial training. The key insight is that conventional approaches that mimic whole-body motions often fail because they don't account for the distinct roles of upper body (motion tracking) and lower body (stable locomotion). The authors formulate this as a two-player zero-sum Markov game where the lower body learns robust locomotion while the upper body tracks various motions, with each acting as an adversary to the other. They demonstrate their approach on the Unitree H1-2 robot, create a large-scale dataset (ALMI-X) with 80K+ trajectories and language annotations, and train a foundation model for end-to-end control.", "questions": "* How does your approach compare to recent work on language-guided robot control, particularly by Mower, Bou Ammar and others (integrated into \"ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning\")? While your method uses language annotations in the dataset, ROS-LLM integrates LLMs directly into the control loop for task reasoning and feedback. Could you discuss the trade-offs between your adversarial RL approach versus LLM-based reasoning for humanoid control?\n\n* The simplified framework samples adversarial commands rather than updating both policies. Have you experimented with true simultaneous policy updates? What are the computational and stability trade-offs?\n\n* Why does the foundation model performance degrade on the full dataset? Have you considered alternative architectures or training strategies to address this limitation?\n\n* How dependent is ALMI on the specific kinematics of the Unitree H1-2? Would it work with a TALOS or a G1 or a Booster? What modifications would be needed to apply it to other humanoid platforms?\n\n* The experiments focus on relatively short motion sequences. How does the method perform on longer-horizon tasks that require extended coordination between upper and lower body?\n\n* What are the primary failure modes of the trained policies? When does the adversarial training fail to find a good equilibrium?", "limitations": "The authors partially address limitations in Section 7, but several key limitations deserve more discussion:\n\n- The paper doesn't discuss the computational requirements for the adversarial training process or how it scales with robot complexity.\n- The approach relies on motion retargeting from human data, which may limit the types of behaviors that can be learned.\n- While teleoperation is demonstrated, the paper doesn't discuss safety mechanisms for autonomous operation in human environments.\n- Although successful sim-to-real transfer is shown, the specific domain randomization required suggests potential brittleness.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n+ The paper includes extensive experiments in simulation and real-world deployment, demonstrating robust performance across various metrics including motion tracking accuracy and locomotion stability.\n+ The method achieves impressive real-world results on the Unitree H1-2, including loco-manipulation tasks with teleoperation, showing clear practical applicability.\n+ ALMI-X represents a valuable contribution to the community - a large-scale humanoid control dataset with language annotations that could enable future research.\n+ Dual curriculum mechanism for adversarial motion sampling is well-designed, starting with moderate disturbances and gradually increasing intensity.\n\nWeaknesses\n- The paper acknowledges simplifying the original max-min problem by sampling adversarial commands rather than updating both policies simultaneously. This may limit the effectiveness of the adversarial training.\n- The foundation model results are preliminary and show degraded performance when trained on the complete ALMI-X dataset versus subsets, suggesting scalability issues.\n- The paper only compares against Exbody and ablations of their own method. Comparisons with other state-of-the-art humanoid control methods would strengthen the evaluation.\n- While the method works well for the specific robot and motions tested, it's unclear how well it would generalize to different humanoid platforms or more complex manipulation tasks.", "was_revised": true, "final_justification": "I have adapted my scores taking the rebuttals into account!", "rebuttal": "We sincerely thank the reviewer for the thoughtful comments and valuable suggestions.\n\n**W1:  Simplification of the max-min problem by sampling adversarial commands**\n\n(i) Simplifying the max-min problem does affect the effectiveness of adversarial training. However, without this simplification, four separate policies would be required in each round of policy learning. Specifically, in round 1, when updating $\\pi^u_1$, an additional adversary $\\pi^l_a$ is required; when updating $\\pi^l_1$, an additional adversary $\\pi^u_a$ is required. It would be computationally expensive to train an additional adversary in each round. (ii) As a result, we change the inner-loop optimization of the adversary from the parameter space to the command space, which allows us to sample adversarial command to approximate the effects of training adversarial policies. (iii) ***We add additional experiments to show the necessity of the simplification***:  \n**1) Train an adversary.**\nIf we do not adopt the simplified approach, we would instead directly train an adversary to attack the policy. **However, it is very challenging to properly control the strength of the adversary**. Take the training of lower body policy as example, we jointly train an upper-body adversary to minimize the lower-body reward. We vary `action_clip_scale` to control adversary strength, where `action_clip` = 100 × `action_clip_scale`. The results are shown in the table below. It can be seen that large adversary collapse training, while weak adversaries yield poor robustness. In contrast, our curriculum-based method, guided by motion difficulty, provides effective and stable adversarial training.\n\n| Upper body action clip scale  | Mean episode length @20k | Mean reward @20k|\n|:-:|:-:|:-:|\n|1|7.0|0.0|\n|0.1|612.3|4.21|\n|0.01|978.5|25.79|\n\n| Method |$E_\\text{vel}\\downarrow$| $E_\\text{ang}\\downarrow$| $E_\\text{jpe}^\\text{upper}\\downarrow$| $E_\\text{kpe}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|ALMI (Ours)|0.2202|0.4812|0.2116|0.0458|0.0600|0.0175| 0.8551|0.9723|\n|`action_clip_scale`=0.1|1.2610|5.3445|\\\\|\\\\|\\\\|6.4459|1.7446|0|\n|`action_clip_scale`=0.01|1.3707|6.8632|\\\\|\\\\|\\\\|6.1924|2.3056|0|\n\n**2) Policy with adversarial goals.**\nAnother way to implement adversarial training is to assign each policy two objectives. For this experiment, we train upper and lower policies jointly, each maximizing its own reward while minimizing the other’s. We vary adversarial strength via reward weights (e.g., **-1 for strong adversary and -0.1 for weak adversary**). Results show adversarial weight greatly affects training, and tuning it is nontrivial. Our simplified method ensures stable and effective adversarial interaction. Among converged settings, our method outperforms others in all metrics.\n\n|Adversary setting|Mean episode length @10k|Mean lower reward @10k|Mean upper reward @10k|\n|-|:-:|:-:|:-:|\n|weak $\\pi_a^u$ + weak $\\pi_a^l$|976.4|21.1|148.7|\n|weak $\\pi_a^u$ + strong $\\pi_a^l$|11.66|0.0|0.0|\n|strong $\\pi_a^u$ + weak $\\pi_a^l$|912.2|10.8|91.7|\n|strong $\\pi_a^u$ + strong $\\pi_a^l$|11.37|0.0|0.0|\n\n|Method| $E_\\text{vel}\\downarrow$| $E_\\text{ang}\\downarrow$| $E_\\text{jpe}^\\text{upper}\\downarrow$| $E_\\text{kpe}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|ALMI (Ours)|**0.2202**|**0.4812**|**0.2116**|**0.0458**| **0.0600**| **0.0175**|**0.8551**|**0.9723**|\n|weak $\\pi_a^u$ + weak $\\pi_a^l$|0.3415|1.4413|0.29469| 0.0526|0.0970|0.0183|1.0250|0.9612|\n|strong $\\pi_a^u$ + weak $\\pi_a^l$|1.1220|4.6715|2.7251| 0.1840|0.2450|4.6937|1.1702|0.3524|\n\n**W2: Scalability of the foundation model**\n\nOur foundation model study is an early step toward exploring direct language-to-action mapping using the ALMI-X dataset. To ensure practical deployability under high-frequency inference, we adopted a lightweight, conservative model design without architectural innovations, which may limit its capacity to fully utilize large-scale data. The observed performance drop on the full dataset reflects this trade-off. These experiments primarily serve to validate the dataset’s potential; future work will explore stronger model architectures, better data processing, and optimized training pipelines. **We will revise the paper to better convey the exploratory nature of this component and moderate our claims**.  \n\n**W3: Limited baseline comparison**\n\nWe have added new baseline methods: OmniH2O, Exbody2 and compared them with our method on Unitree G1 robot, due to space limits, please refer to our **response to Reviewer RhvN, W3**.\n\n**W4: Generalize to different humanoid platforms or more complex manipulation tasks** \n\n**(1)** We have included experiments on Unitree G1 robot. Please refer to the project page. Generalizing to a different robot mainly requires **(a) retargeting MoCap data to the new robot**, and **(b) making minor adjustments to the reward function** to encourage more natural gait patterns. \n\n**(2)** As for more complex manipulation tasks, our experiments’ tasks—vacuuming, door opening, and box carrying—are challenging mobile manipulations where upper-body motion affects lower-body stability. Our modular framework also allows easy swapping of the upper-body policy with pre-trained models like VLA for better generalization and flexibility.\n\n**Q1: Comparison with recent work on language-guided robot control**\n\nThank you for pointing out this relevant line of work. ROS-LLM primarily focuses on robotic arms and does not involve whole-body humanoid control, where coordinated lower- and upper-body behaviors are essential. Our approach can complement such frameworks by providing low-level motor skills for complex whole-body tasks. Moreover, the language descriptions in ALMI-X typically correspond to holistic motion patterns, which are not easily decomposed into modular subgoals that LLMs can reason over. This limits the applicability of structured language reasoning in these contexts. Finally, as noted in prior works [1,2], current LLMs exhibit limited capacity for precise spatial reasoning—especially in generating coherent 3D motion sequences involving poses and rotations.  \n[1] Spatialllm: A compound 3d-informed design towards spatially-intelligent large multimodal models. CVPR 2025  \n[2] Evaluating spatial understanding of large language models. TMLR 2025\n\n**Q2:   Simultaneous policy updates**\n\nWe have added experiments about the  simultaneous updating issue, please refer to the **response to W1**. \n\n**Q3: Foundation model performance on the full dataset**\n\nDue to the space limit, please refer to our **response to W2**\n\n**Q4: Generalize to different humanoid platforms**\n\nPlease refer to our **response to W4**.\n\n**Q5: Performance on longer-horizon tasks**\n\nThank you for the question. (i) For lower-body locomotion, the adversarial difficulty depends on survival length and the motion scale. Thus, longer sequences do not directly increase the coordination difficulty. (ii) For upper-body tracking, we adopt a frame-by-frame motion tracking policy; thus, the reward function can also accurately capture the tracking accuracy even with long motion sequences. In addition, the motion tracking is only conducted in the upper body, which reduces the difficulty of motion tracking. In our experiments, we find that tracking long motions does not affect the tracking performance. We’ve also tested longer AMASS motions on the G1 robot.\n\n**Q6: Primary failure modes and training failure cases**\n\n(i) **Inference-time** failure modes mainly arise from common edge cases in humanoid robot control, such as carrying excessively heavy loads or unexpected actuation delays, which may exceed the training distribution. (ii) During **training**, adversarial learning without curriculum often leads to unstable training process. Moreover, insufficient training iterations may prevent the upper and lower body policy from reaching a stable equilibrium.\n\n**L1: Computational requirements and how it scales with robot complexity**\n\nWe report the training time of ALMI for both H1-2 and G1 robots, all trained on an NVIDIA RTX 4090, as shown in table below.\n\n|ALMI (H1-2)|ALMI (G1)|\n|-|-|\n|Lower-1\\~3: 10k, 6k, 5k iterations with 2.5s/iter; Upper-1\\~2: 3k, 1k iterations with 2.0s/iter;  **Total: about 16.8 hours**|Lower-1\\~3: 10k, 8k, 6k iterations with 2.5s/iter; Upper-1\\~2: 3k, 3k iterations with 2.0s/iter;  **Total: about 20.0 hours**| \n\n**L2: Motion retargeting from human data**\n\nThank you for the comment. Although our method relies on motion retargeting from human data, the use of the diverse AMASS dataset ensures good generalization. We also validated the policy on the real H1-2 robot via teleoperation, showing it can mimic various upper-body motions, suggesting that the human data dependency does not significantly limit behavior diversity.\n\n**L3: Safety mechanisms**\n\nThank you for the suggestion. Given H1-2's whole-body scale, safety is crucial. We enforced safe joint velocity and workspace limits for all end-effectors and included an emergency stop to disable the policy when needed.\n\n**L4: About domain randomization**\n\nWe acknowledge the concern. Domain randomization helps bridge the sim-to-real gap by compensating for imperfect simulation and real-world variations like torque noise and environment changes, making it essential for robust transfer [1].  \n[1] Peng, Xue Bin, et al. Sim-to-real transfer of robotic control with dynamics randomization. ICRA, 2018\n\n---\nWe hope these clarifications have fully addressed your concerns. If so, we would deeply appreciate your consideration in raising your score. If you have any further suggestions, please feel free to reach out."}, {"review_id": "ottJneyaBn", "reviewer": "Reviewer_xGSi", "summary": "This paper presents ALMI (Adversarial Locomotion and Motion Imitation), a novel framework for humanoid control that separately trains upper-body and lower-body policies in an adversarial setup. During training, the lower body learns to perform robust locomotion under adversarial disturbances from the upper body, while the upper body learns to track motions accurately despite unstable base movement. The algorithm is validated on the full-size Unitree H1-2 robot. The authors also introduce ALMI-X, a dataset with over 80K trajectories labeled with natural language descriptions. Experiments are conducted in both simulation and real-world settings.", "questions": "The policies are trained and tested in Isaac Gym, yet the dataset is collected in MuJoCo. What’s the motivation or specific consideration behind using MuJoCo for ALMI-X data collection?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe core idea of treating upper-body and lower-body control as adversaries is novel and interesting. To the best of my knowledge, this is the first time such adversarial interplay has been used to encourage robustness in humanoid control.\n\nThe separation of motion roles (locomotion vs. imitation) helps stabilize policy learning.\n\nThe authors release a dataset (ALMI-X) with language annotations, which could potentially support future work in language-conditioned whole-body control.\n\nWeaknesses:\n\nWhile the adversarial framework is promising, it is inherently limited to motion decomposition (i.e., clear separation between upper- and lower-body tasks). It may not generalize well to agile whole body coordination.\n\nThe foundation model component feels disconnected from the core contribution. If I understand correctly, the model is trained in a supervised fashion on the collected ALMI-X dataset using language annotations, but the results are still far from a true \"foundation model\" for whole-body control.\n\nIn comparison, stronger motion generation performance might be achieved by combining diffusion-based human motion synthesis with a physics-based motion tracking controller.\n\nThe real-world robot experiments appear overly conservative, especially in locomotion behavior. The demo videos on the project page mostly showcase the ALMI policy, not the foundation model. Is that correct?", "was_revised": true, "final_justification": "The authors have addressed my concerns in the rebuttal. I think the idea is interesting and the experimental results are reasonable.", "rebuttal": "We sincerely appreciate your thorough review and constructive feedback.\n\n**W1: Limitation of motion decomposition**\n\nWe thank the reviewer for the insightful comment. We agree that a clear separation between upper and lower-body tasks may constrain the expressiveness required for agile, whole-body coordinated motions. Indeed, end-to-end policy learning over the full body’s degrees of freedom has the potential to produce more fluid and dynamic behaviors.\n However, our work focuses on mobile manipulation with full-size humanoid robots, such as the H1-2, which involves challenging scenarios like lifting and transporting heavy objects. In such tasks, **achieving robustness is particularly critical**. We found that end-to-end policies often struggle to generalize under unseen disturbances from arm motions, which can severely affect balance. By decoupling the control of the upper and lower body, ALMI improves robustness by allowing the lower-body controller to maintain balance despite diverse, unseen upper-body behaviors.\nAdditionally, the **modularity** of our framework brings practical benefits. For example, it allows the upper-body policy to be easily swapped out for a pre-trained manipulation model like VLA, enabling better generalization and task flexibility. As demonstrated in our supplementary video (e.g., door-opening tasks), ALMI is still capable of achieving coordinated behaviors across the full body, suggesting that despite the decomposition, our approach maintains a reasonable degree of whole-body integration.\nWe will **revise the main text to clarify this design trade-off** and better position ALMI as a practical, robust solution for mobile manipulation, while **acknowledging its limitations for highly agile full-body coordination**.\n\nWe also added two popular whole-body motion tracking methods to the baseline experiments: OmniH2O [1], Exbody2 [2] and compared them with our method on Unitree G1 robot.\n\n- **Experiment Design and Evaluation Metrics.** We adopt the CMU MoCap dataset with $1122$ motion clips to evaluate different metrics, which are same as our baseline experiment in paper, in IsaacGym. As OmniH2O and Exbody2 are imitation-based whole-body control methods without velocity tracking, so, for ALMI, we used linear and angular velocity provided by motion dataset as velocity tracking command; for the other methods, they directly track motions.  \n\n - The comparative results are shown as follows. The experimental results show that: 1) Compared with the current popular imitation-based whole-body control methods, our iterative adversarial upper and lower body method is still promising in tracking accuracy and robustness. 2) Our method has excellent scalability for the robot platform. \n\n|Method|$E_\\text{vel}\\downarrow$| $E_\\text{ang}\\downarrow$| $E_\\text{jpe}^\\text{upper}\\downarrow$| $E_\\text{kpe}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|ALMI (Ours)|**0.1396**|**0.2776**|**0.2367**|**0.0411**| **0.0198**|**0.7411**|**0.0977**|**0.9484**|\n|OmniH2O|0.1615|0.4166|1.0826|0.0598|1.2773|2.3219| 0.1696|0.3882|\n|Exbody2|0.4015|0.6066|0.3821|0.0719|1.1797|1.3547| 0.3367|0.8848|\n\n[1] He, Tairan, et al. \"Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning.\" arXiv preprint arXiv:2406.08858.  \n[2] Ji, Mazeyu, et al. \"Exbody2: Advanced expressive humanoid whole-body control.\" arXiv preprint arXiv:2412.13196.\n\n**W2: Limited capacity of the foundation model**\n\nThank you very much for pointing out this issue. As mentioned in our paper, the capabilities of the foundation model still have considerable room for improvement, and we will revise the manuscript to **tone down the claims** regarding this part accordingly.\nIt is also worth noting that training a whole-body humanoid control model for language-to-action purely through supervised learning is highly challenging, as humanoid robots exhibit high degrees of freedom and are subject to frequent state discontinuities caused by intermittent foot contacts. Our experiments represent a preliminary attempt to explore this difficult yet promising direction using the ALMI-X dataset. We hope that ALMI-X and our experimental results can provide useful insights for future research in this area. However, it is important to clarify that solving this problem is not the primary focus of this study.\n\n**W3: Comparison with motion generation + motion tracking**\n\nWe sincerely thank the reviewer for the thoughtful comments. The paradigm of combining diffusion-based human motion synthesis with a physics-based motion tracking controller is indeed capable of producing more diverse and expressive motions. However, current motion generation methods often do not sufficiently account for the physical constraints of the real world. In practice, these motions typically require retargeting process to adapt them to the robot’s embodiment, and directly tracking them with a motion tracking controller rarely succeeds without extensive motion repair and filtering processes which significantly hinder large-scale motion generation and real-world policy deployment. Although some existing works have achieved similar functionalities, such as Bumblebee [1], they often lack robustness—particularly in tasks requiring interaction with environment like mobile manipulation.\nIn contrast, our method enables robust execution of relatively stable motions and supports large-scale generation, offering a more practical solution under real-world physical constraints.\n\n[1] Wang, Yuxuan, et al. \"From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots.\" arXiv preprint arXiv:2506.12779 (2025).\n\n**W4: Real-world experiments appear to be conservative**\n\nThank you very much for the constructive feedback. (i) The mobile manipulation tasks demonstrated in our video—such as vacuuming, carrying boxes, and operating a refrigerator—involve interaction with external objects, which introduces disturbances during execution. These tasks require the robot to constantly maintain balance while performing the manipulation. Although such tasks may appear more conservative compared to highly dynamic behaviors like dancing—which are implemented using motion tracking techniques. These methods cannot interact with external objects and often suffer from poor robustness.(ii) The demonstrations shown in our video are performed using the ALMI policy rather than the foundation model. The foundation model we trained is intended as an initial exploration into the challenging and under-explored problem of language-to-action humanoid robot control, while also serving to validate the practicality of the ALMI-X dataset. As such, this part of the work remains preliminary. The model’s performance in simulation is still unstable and not yet suitable for large-scale deployment. \n\n**Q1: Motivation of collecting data in MuJoCo**\n\nThanks for the question. According to Sec. 6 and Sec. B.5 in our paper, we follow the common practice in this field and adopt sim-to-sim transfer (i.e., Isaac to MuJoCo) to cross-validate our policy. Specifically, IsaacGym can utilize NVIDIA’s GPU resources to enable highly parallel training, while MuJoCo is specifically designed for physics simulation and offers stable and reliable results for sim-to-real transfer. As a result, we adopt MuJoCo to generate more accurate robot trajectories to facilitate sim-to-real transfer in future work.\n\n---\nWe hope these clarifications have fully addressed your concerns. If so, we would deeply appreciate your consideration in raising your score. If you have any further suggestions, please feel free to reach out."}, {"review_id": "NejMHUYavT", "reviewer": "Reviewer_XwBe", "summary": "Training curriculum for whole body humanoid controller has been an active area of research lately. This paper proposes ALMI (Adversarial Locomotion and Motion Imitation) . They address this by first treating lower and upper bodies separately. then by training upper and lower body contrllers separately while dilating the other policy to be adverserial to learning of the other policy.  the instantiatione implicitly assumes that lower bod precise footstep and movement is not needed and we only care about velocity. Additionally, the authors release ALMI-X, the first large-scale whole-body control dataset with 80K+ trajectories and language descriptions, enabling foundation model training for humanoid control.", "questions": "One of my main reason for rejection is that I am not quite sold on the notion that upper and lower body needs to be *trained separately*. I do understand that having different policy heads or networks for upper and lower body may have some benefits and should be treated differently in the reward design, but I don't see why they cannot be trained together with some clever sampling of upper and lower body command directives. I am happy to hear some counter arguments to this and am willing to change my score in rebuttal.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "*Strengths:*\n- Code has been provided and is clean with enough intructions for reproducibility. The details provided in the paper is also good. \n- Mujoco dataset shows sim2sim transfer and the dataset collected is good and showcases that such can be used as a dataset for training foundation model directly that can do sim2real transfer. training policy in high throughput sim, and using that to collect data in high fidelity sim to transfer to real world is a good approach. \n- having adversarial curriculum is an intuitive approach to having more robust policy. \n\n*Weakness:* \n- Overall the novelty of the paper boils down to iterative adversarial upper and lower body commands curriculum. It is not clear how much effective it is as compared to other innovations already done in whole body imitation controllers like Masked Humanoid Controller, ExBody2, OmniH20, Hover, MaskedMimic to name a few. It could be wonderful to see some of these baselines for fair comparison. \n- The iterative process takes longer time for training, the compute need of the approach is not clear. does all approaches in the baseline use the same amount of compute and training time, if not it has to be clear when comparing the baselines. \n- There should be a way to do the iterative approach in an integrated manner with a smaller window for switching between upper and lower bod policy. having a fixed number of iteration seems a bit restrictive and compute heavy.", "was_revised": true, "final_justification": "The splitting of the model and adversarial sampling of training distribution is novel enough and does not seem to come with heavy computation costs and could be complementary to other training curriculums/approaches.", "rebuttal": "Thank you for your valuable feedback and insightful comments. We appreciate the opportunity to clarify and improve our work.\n\n**W1: More baseline comparison**\n\nThank you for the suggestion. We have added new baseline methods: OmniH2O, Exbody2 and compared them with our method on Unitree G1 robot. Due to space limits, please refer to our **response to Reviewer RhvN, W3**.\n\n**W2: Computational cost**\n\nThank you for your insightful comment. The table below provides a detailed list of the training times and selected checkpoints for the methods being compared in the baseline experiments. \n\n||Training Time Consumption|Experiment Checkpoint|\n|-|-|-|\n|**H1-2**|\n|ALMI (Ours)|Lower-1\\~3: 10k, 6k, 5k iterations with 2.5s/iter; Upper-1\\~2: 3k, 1k iterations with 2.0s/iter;  **Total: about 16.8 hours**|Lower-3 5k iter + Upper-2 1k iter|\n|ALMI (Whole)|20k iterations with 3.0s/iter; **Total: about 16.6 hours**| 20k iter|\n|ALMI (w/o curriculum)|Lower-1\\~3: 15k, 8k, 6k iterations with 2.5s/iter; Upper-1\\~2: 5k, 3k iterations with 2.0s/iter; **Total: about 24.5 hours**|Lower-3 6k iter + Upper-2 3k iter|\n|ALMI (Lower1)|Same as ALMI (Ours)|Lower-1 10k iter + Upper-2 3k iter|\n|ALMI (Lower2)|Same as ALMI (Ours)| Lower-2 6k iter + Upper-2 3k iter|\n|Exbody|25k iterations with 2.0s/iter; **Total: about 13.8 hours**|25k iter|\n|**G1**|\n|ALMI (ours)|Lower-1～3: 10k, 8k, 6k iterations with 2.5s/iter; Upper-1～2: 3k, 3k iterations with 2.0s/iter. **Total: about 20.0 hours**|Lower-3 6k iter + Upper-2 3k iter|\n|OmniH2O|Teacher: 30k iterations with 1.5s/iter; Student: 10k iterations with 1.5s/iter; **Total: about 16.6 hours**|Student 10k iter|\n|Exbody2|Teacher: 20k iterations with 2.5s/iter; Student: 4k iterations with 2.0s/iter; **Total: about 16.1 hours**|Student 4k iter|\n\n**W3: An integrated manner for switching policy updating**\n\nWe sincerely thank the reviewer for this insightful suggestion. We agree that a fixed iteration number for alternating between upper and lower body policies can be restrictive and computationally inefficient. \n\nTo address this, we designed an automatic switching mechanism. Specifically, both the upper and lower policies are initialized at the start of the training, but only one policy is updated per phase. We monitor the ratio of the current reward to its maximum, and when it surpasses a certain switching threshold (determined based on empirical observations), the PPO runner switches to update the other policy. Preliminary results are shown in the below.\n\n|Method|$E_\\text{vel}\\downarrow$|$E_\\text{ang}\\downarrow$|$E_\\text{jpe}^\\text{upper}\\downarrow$|$E_\\text{kpe}^\\text{upper}\\downarrow$|$E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$ | $\\text{Survival}\\uparrow$|\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|ALMI (Ours)|**0.2202**|**0.4812**|**0.2116**|**0.0458**|0.0600| **0.0175**|**0.8551**|**0.9723**|\n|Auto Switch @10k|0.8693|1.2887|1.0391|0.0953|0.0843| 0.6747|0.9253|0.3458|\n|Auto Switch @20k|0.3677|0.9629|0.2616|0.0669|0.0674| 0.0582|0.8532|0.9389| \n|Auto Switch @30k|0.3251|0.9433|0.2563|0.0593|**0.0584**| 0.0361|0.9155|0.9402|\n\nWe find this automatic switching strategy effective, as it simplifies the training workflow and reduces overall training time. However, it is worth noting that the approach is sensitive to the choice of switching criteria. If the threshold or reward metric is not well-tuned, the resulting policy may suffer from suboptimal performance. Moreover, in real-world robot control, higher rewards don't always correlate perfectly with better physical execution due to sim2real gaps.\nOverall, we believe the robust automatic switching mechanisms within adversarial training frameworks for humanoid control is an important and meaningful research avenue.\n\n**Q1: Issue about separately training upper and lower policy**\n\nWe sincerely thank the reviewer for the thoughtful comments. We understand the reviewer's confusion, as some prior works (e.g., Exbody) have indeed achieved upper-body tracking and lower-body locomotion in a single training stage by assigning separate reward functions to different body parts. However, the focus of our work lies in enhancing the robustness of robot control through adversarial interactions between the upper and lower body. To this end, we introduce the theoretical framework of a two-player Markov game.\nWe will first briefly explain this framework, and then use two newly added experiments to illustrate why we adopt the proposed approach in our paper. \n(i) According to our theoretical analysis, the two policies can be considered as two players in a Markov game in policy learning. Specifically, when learning the lower-body policy $\\pi^l$ by maximizing it value function, the upper-body policy $\\pi^u$ is optimized to minimize it and serves as an adversary. However, to solve such a min-max problem and obtain a Nash equilibrium, the update should follow a specific two-scale learning rate and independent policy gradients according to Theorem 3.1. (ii) In practice, if we use a single network to represent $\\pi^l$ and $\\pi^u$, the two policies are hard to obtain independent policy gradients or using two-scale learning rate, which can lead to unstable policy updates. (iii) Additionally, we **add two experiments** to further clarify our design choices:  **1)** why we simplify the adversarial process using a command-based curriculum and **2)** why the two policies are trained separately.\n\n**1) Train an adversary.**\nIf we do not adopt the simplified approach, we would instead directly train an adversary to attack the policy. **However, it is very challenging to properly control the strength of the adversary**. Taking the training of lower-body policy as an example, we jointly train an upper-body adversary whose objective is to minimize the reward of the lower-body policy. \nTo reflect different levels of adversarial strength, we vary the `action_clip_scale` parameter. Specifically, the upper-body's action is clipped using a scaled range:\n`actual_action_clip = 100 × action_clip_scale`. The results are shown in the table below. We observe that different settings of `action_clip_scale` significantly affect the training outcome. With `action_clip_scale=1`, the adversary tends to produce large action outputs, which quickly cause the robot to fail and eventually lead to training collapse.\nA moderate `action_clip_scale` creates strong interference for the lower-body policy—resulting in slow reward improvement—but does not entirely destabilize the policy. In contrast, a small `action_clip_scale` leads to weak adversarial influence, with negligible impact on the lower policy’s performance. \n\nThis experiment demonstrates that the lower policy is highly sensitive to the strength of adversarial attacks. When the adversary and the policy are updated simultaneously, the adversary must not be too strong to prevent training collapse, yet still provide sufficient challenge to enhance the robustness of the lower policy.\nTherefore, adopting a curriculum-based adversary is an intuitive and effective solution. Our approach—using the difficulty of reference motions as the basis for the curriculum—**not only ensures coverage of common robot behaviors, but also enables progressive adversarial training**.\n\n| Upper body action clip scale  | Mean episode length @20k | Mean reward @20k|\n|:-:|:-:|:-:|\n|1|7.0|0.0|\n|0.1|612.3|4.21|\n|0.01|978.5|25.79|\n\n|Method|$E_\\text{vel}\\downarrow$| $E_\\text{ang}\\downarrow$| $E_\\text{jpe}^\\text{upper}\\downarrow$| $E_\\text{kpe}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{upper}\\downarrow$| $E_\\text{action}^\\text{lower}\\downarrow$| $E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|ALMI (Ours)|**0.2202**|**0.4812**|**0.2116**|**0.0458**|**0.0600**|**0.0175**|**0.8551**|**0.9723**|\n|`action_clip_scale`=0.1|1.2610|5.3445|\\\\|\\\\|\\\\|6.4459|1.7446|0|\n|`action_clip_scale`=0.01|1.3707|6.8632|\\\\|\\\\|\\\\|6.1924|2.3056|0|\n\n**2) Policy with adversarial goals.**\nAnother way to implement adversarial training is to assign each policy two objectives. For this experiment, we train upper and lower policies jointly, each maximizing its own reward while minimizing the other’s.\nWe vary adversarial strength via reward weights (**e.g., -1 for strong adversary and -0.1 for weak adversary**). The experimental results are shown in the table below. It can be observed that the weight of the adversarial goal also has a significant impact on training, and selecting an appropriate weight  is nontrivial. The simplified approach adopted in our work provides an efficient way to ensure both effective adversarial interaction and training stability.\n\n|Adversary setting|Mean episode length @10k|Mean lower reward @10k|Mean upper reward @10k|\n|-|:-:|:-:|:-:|\n|weak $\\pi_a^u$ + weak $\\pi_a^l$|976.4|21.1|148.7|\n|weak $\\pi_a^u$ + strong $\\pi_a^l$|11.66|0.0|0.0|\n|strong $\\pi_a^u$ + weak $\\pi_a^l$|912.2|10.8|91.7|\n|strong $\\pi_a^u$ + strong $\\pi_a^l$|11.37|0.0|0.0|\n\n|Method|$E_\\text{vel}\\downarrow$|$E_\\text{ang}\\downarrow$|$E_\\text{jpe}^\\text{upper}\\downarrow$|$E_\\text{kpe}^\\text{upper}\\downarrow$|$E_\\text{action}^\\text{upper}\\downarrow$|$E_\\text{action}^\\text{lower}\\downarrow$|$E_\\text{g}\\downarrow$|$\\text{Survival}\\uparrow$|\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|ALMI (Ours)|**0.2202**|**0.4812**|**0.2116**|**0.0458**| **0.0600**| **0.0175**|**0.8551**|**0.9723**|\n|weak $\\pi_a^u$ + weak $\\pi_a^l$|0.3415|1.4413|0.29469| 0.0526|0.0970|0.0183|1.0250|0.9612|\n|strong $\\pi_a^u$ + weak $\\pi_a^l$|1.1220|4.6715|2.7251| 0.1840|0.2450|4.6937|1.1702|0.3524|\n---\nWe hope these clarifications have fully addressed your concerns. If so, we would deeply appreciate your consideration in raising your score. If you have any further suggestions, please feel free to reach out."}]

Author rebuttal:

Meta-review: This paper tackles the problem of learning a whole-body controller by decomposing the problem into learning separate controllers for lower- and upper-body motion. This learning setup is done through an adversarial optimziation process as a zero-sum game: the locomotion/lower-body policy seeks to maximize the ability to follow locomotion commands, and the upper-body controller seeks to minimize disturbances. The lower-body must also account for potentially adversarial upper-body motions. The reviewers agree with the authors' claim that the approach is novel and that the dataset is interesting. The real-world deployment and positive results were also strengths. Reviewers had concerns that important technical details were relegated to the appendix. Further, the paper did not adequately benchmark against state-of-the-art baselines. The authors appear have succesfully rebutted the benchmarking concern by adding baselines and showing positive reuslts. If this is incorporated into the paper, that would be helpful. Overall, the paper seems to make a contribution to the whole-body locomotion literature even if the adversarial setup may not fully take advantage of the complete capabilities of such a system (i.e., cooperative optimization).
Decision: Accept (poster)