BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning
Authors: Yitang Li
Zhengyi Luo
Tonghe Zhang
Cunxi Dai
Anssi Kanervisto
Andrea Tirinzoni
Haoyang Weng
Kris Kitani
Mateusz Guzek
Ahmed Touati
Alessandro Lazaric
Matteo Pirotta
Guanya Shi
Venue: ICLR 2026 Poster (2026)
Keywords: Unsupervised Reinforcement Learning
Robotics
Humanoid Robot
Robotics Foundation Model
Primary area: applications to robotics, autonomy, planning

Abstract: Building Behavioral Foundation Models (BFMs) for humanoid robots has the potential to unify diverse control tasks under a single, promptable generalist policy. However, existing approaches are either exclusively deployed on simulated humanoid characters, or specialized to specific tasks such as tracking. We propose BFM-Zero, a framework that learns an effective shared latent representation that embeds motions, goals, and rewards into a common space, enabling a single policy to be prompted for multiple downstream tasks without retraining. This well-structured latent space in BFM-Zero enables versatile and robust whole-body skills on a Unitree G1 humanoid in the real world, via diverse inference methods, including zero-shot motion tracking, goal reaching, and reward inference, and few-shot optimization-based adaptation. Unlike prior on-policy reinforcement learning (RL) frameworks, BFM-Zero builds upon recent advancements in unsupervised RL and Forward-Backward (FB) models, which offer an objective-centric, explainable, and smooth latent representation of whole-body motions. We further extend BFM-Zero with critical reward shaping, domain randomization, and history-dependent asymmetric learning to bridge the sim-to-real gap. Those key design choices are quantitatively ablated in simulation. A first-of-its-kind model, BFM-Zero establishes a step toward scalable, promptable behavioral foundation models for whole-body humanoid control. Webpage:
https://lecar-lab.github.io/BFM-Zero/

Published as a conference paper at ICLR 2026
BFM-ZERO: A PROMPTABLE BEHAVIORAL FOUNDA- TION MODEL FOR HUMANOID CONTROL USING UN- SUPERVISED REINFORCEMENT LEARNING
Yitang Li Zhengyi Luo2.. Tonghe Zhang Cunxi Dai
Andrea Tirinzoni
Anssi Kanervisto' Haoyang Weng Kris Kitani Mateusz Guzek
Ahmed Touati
Alessandro Lazaric
Matteo Pirotta 3. Guanya Shi
"qqual Contribution. Equal Contribution. 'Equal advising. IIIS, Tsinghua University (Work done during authors' internship CMU)
*Carnegie Mellon University
"Meta
(A)
Unseen Motion
Tracking
Reward Optimization
2, B(Secoa)
B(s)
BBM Zero
Natural Recovery
getual
Goal Reaching
Running Pattern
(C)
Few-shot Add
Adaplation
Adaptation
4kg
objective
(E)
Figure BFM-Zero enables versatile and robust whole-body skills. (A-C) Diverse zero-shot inference methods. (D) Natural recovery from large perturbation. (E) Few-shot adaptation.
ABSTRACT
Building Behavioral Foundation Models (BFMs) for humanoid robots has the tential to unify diverse control tasks under a single, promptable generalist pol- icy. However, existing approaches are either exclusively deployed simulated humanoid characters, or specialized to specific tasks such as tracking. We pro- pose BFM-Zero, framework that learns effective shared latent represen- tation that embeds motions, goals, and rewards into a common space, enabling single policy to be prompted for multiple downstream tasks without retrain- ing. This well-structured latent space in BFM-Zero enables versatile and robust whole-body skills on Unitree GI humanoid in the real world, via diverse in- ference methods, including zero-shot motion tracking, goal reaching, and reward inference, and few-shot optimization-bant based adaptation. Unlike prior on-policy re- inforcement learning (RL) frameworks, BeM-zero builds upon recent advance- ments in unsupervised RL and Corward-Backwerr (FB) models, which offer an objective-centric, explainable, and smooth latent representation of whole-body motions. We further extend BFM-Zero with critical reward shaping, domain randomization, and distory-depender asymmetric learning to bridge the sim-to- real gap. Those key design choices are quantitatively ablated in simulation. first-of-its-Ke model, BFM-Zero establishes step toward scalable, prompt-

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Published as a conference paper at ICLR 2026
able behavioral foundation models for wodle-body humanoid control. Webpage: https://lecar giggiithhhh 1w/.BM//////e...
INTRODUCTION
Humanoid robots have the potential to transform numerous aspects of our daily lives, from manufac- turing and logistics to healthcare and personal assistance. However, realizing this potential requires robots to perform a wide range of tasks in dynamic and unstructured environments. Humanoid whole-body control is a fundamental and challenging problem in robotics, serving as the first step to enable the humanoids to work safely in human environments (Gu et al., 2025).
In robotics, foundation models have the potential to unify diverse control objectives under a single policy, allowing robots to adapt to new tasks in a zero-shot way or with efficient post-training. The closest approaches to such paradigms are Vision-Language-Actinnn Action (VLA) models for robotic manipulations (e.g.. Ghosh et al., 2024; Intelligence et al., 2025; Kim et al., 2024; Zhong et al., 2025; Team et al., 2025; Bjorck et al., 2025) that learn from human demonstrations (i.e., behavior cloning). However, for humanoid whole-body control, there is a fundamental mismatch that limits direct behavior cloning: unlike manipulation tasks, there are no readily available actuator-level action labels or large-scale teleoperation datasets.
For whole-body humanoid control, most recent advancements follow the sim-to-real pipeline and rely on reinforcement learning (RL) to train policies in simulation before transferring them to hard- ware (Gu al., 2025). Following the success of RL-based motion tracking in physics-based charac- ter animation (e.g.. Luo et al., 2024; Tessler et al., 2024; Tirinzoni et al., 2025), recent works (e.g.. Zakka al., 2025: Seo al., 2025; Chen al., 2025; Liao al., 2025; He al., 2025a; Cheng et al., 2024; He et al., 2025b) have shown remarkable results in transferring policies trained in simulation to real robots. However, most of these approaches rely on on-policy policy gradient methods (c.g., PPO (Schulman et al., 2017)) with explicit racking-based rewards and suffer from major limita- tions. First, they remain task ssee-speiiiic: most policies are trained to explicitly imitate motion capture clips or solve single task. Second, they are non-adaptive: once trained, policies cannot be easily fine-tuned or composed for new tasks. Third, they lack unified and explainable interface for goal specification and behavior composition, making it difficult for human operators to direct the robot or combine learned skills into new behaviors.
In this work, we investigate whether off-policy unsupervised RL can be suitable approach to train so-called Behavioral Foundation Models (BFMs) for whole-body control of a humanoid robot, en- abling it to solve wide range of downstream tasks specified by rewards, goals, demonstrations without retraining. For tasks that require retraining, the BFM should enable efficient post-training. This conjecture is far from trivial. First, most existing methods with real-world deployment rely on on-policy training (primarily PPO), and there is little evidence that off-policy Baaning-commet used in unsupervised RL for training multi-task policies- well suited to this context. Second, no evidence exists that unsupervised RL algorithms can handle the sim-to-real gap and dynamic disturbances robustly, either during simulation policy training or at real-world inference.
We develop BFM-Zero?, an online off- unsupervised RL algorithm that leverages motion capture data to regularize the process of learning generalist whole-body control policies towards human behaviors. We introduce domain randomization to address the sim-to-real gap and train robust policies via asymmetric history-dependent training, leveraging the privileged information available in simulation. Additionally, we incorporate auxiliary rewards to ensure that the learned behaviors adhere to the safety and operational constraints of the physical robot. To the best of our knowledge, the resulting algorithm allows to train the first behavioral foundation model for real humanoids that can be prompted for different tasks (e.g., reward optimization, pose reaching, and motion tracking) without retraining (i.e., in zero-shot). Such flexible and ready-to-use model, paves the way to fast adaptation, fine-tuning or even high-level planning. We validate our approach in both simulated environments and on a real Unitree GI humanoid (Fig. for examples), demonstrating
Zero-shot means that, after pre-training, the policy can he directly deployed in the real world without further interacting with either simulated or real environments. In contrast, few-shot means the policy needs to interact with the environment to collect new data in few episodes to improve on certain tasks.
Zero comes from its zero-shot inference capability via unsupervised RL. and it is first-of-its-kind model.
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robust generalization across tasks and conditions, and showing that even when the zero-shot policy not satisfactory, we can effectively improve it in a matter few episodes interacting with the environment. Related work discussion is available in Section A.
2 BFM-ZERO FOR HUMANOID WHOLE-BODY CONTROL
Pre-Training
Zero-Shot Inference
Diac.
duserstciator
Trajectory Eablediing
Tracking
Open
QD
Roplay Buffer
Auxiliary R
Auclltary
FLLEY
Joant Lindt
Notson
Fau-Shot Adaptation
QF
Plac Ause
Replay Buffeer
Single Pose
Poltcy
Domnin
Randomssation
Sutavvorr
Sequence
Figure 2: An overview of BFM-Zero framework. After the pre- pre-training stage, BFM-Zero forms latent space that can be used for zero-shot reward optimization, single-frame goal reaching, and tracking. can also he adapted in a few-shot fashion to reach more challenging poses.
In this section, we outline the pipeline for training 3FM-Zero in simulation and transferring it to real humanoids. Unlike for virtual characters (e.g., Peng et al., 2022; Tessler et al., 2023; Tirin- zoni et al., 2025), applying unsupervised RL to real humanoids has not yet been attempted. Our BFM-Zero framework consists of an unsupervised pre-training stage, a zero-shot inference proce- dure, and possibly a fast-adaptation post-training stage (as shown in Fig. 2). Section 2.1 provides an overview of unsupervised RL using the BFM-Zero. Section 2.2 details BFM-Zero pre-training, whose objective is to leam unified latent representation that embeds tasks (e.g., target motions, rewards, goals) into shared space C Rd and promptable policy that conditions this representation to perform diverse behaviors without task-ipecific retraining. Then, for downstream tasks during inference, we embed the task into the latent space and use the policy execute the task in zero-shot manner. We also show that efficiently adapt the zero-shot policy in the latent space Z to improve performance on unseen tasks that are not easily covered by zero-shot inference sampling-bass based optimization.
representation framework adopted by
Problem formulation. We formulate real-world humanoid control as a partially observable Markov decision process (POMDP) defined by the tuple (S, O, A, P,)), where S is the full state space, O is the observation space, is the action space, P(8++1181, ag) is the transition dynamics, and 7 E (0, 1) the discount factor. For the 29-degree-of offfreeoo- (DoF) humanoid. the action E A 29 contains the proportional derivative (PD) controller targets for all DoFs. The privileged information
(s E R2463 consists of root height, body pose, body rotation, and linear and angular velocities. The observable state = {qt Ôt, (e.ww woot 04.00t/4..0 /4, E TR is defined as joint position q: E 29 normalized w.r.t. the nominal position q. joint velocity di 29 root angular velocity root g and root projected gravity St. E I We denote by or, {or-H, d.- 0z) E H +61 the observable history composed by proprioceptive state and action. All the components of the states (except root height) normalized w.r.t. the current facing direction and root position. At pre- trainig, we assume that the agent has access to a dataset of unlabeled motions M = {7), which contains observation and privileged states trajectories i. (01, (1,,81,.., (1)) 8(())-
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2.1 UNSUPERVISED RL WITH FORWARD-BACKWARU REPRESENTATIONS
During the pretraining phase, BFM-Zero learns a compact representation of the environment by observing online reward-free interactions in the simulator and leveraging an offline dataset of unla- beled behaviors, resulting in a model that can be prompted to tackle a wide range of downstream tasks (e.g., tracking or reward maximization) in zero-shot manner. To achieve this, we build on top of the recent FB-CPR algorithm (Tirinzoni et al., 2025) which combines the -awward-acckwrd (FB) method for zero-shot RL (Touati & Ollivier, 2021) with online training and policy regulariza- tion on motion-capture data. This method falls in the broader category of unsupervised RL based on successor features (e.g., Touati & Ollivier, 2021; Touati et al., 2023; Pirotta et al., 2024; Park et al., 2024; Agarwal et al., 2024), which involves three components: (i) a latent task feature e : S - Rd that embeds observation s E S into a d-dimensional vector, (ii) a policy π. S - conditioned on a latent vector R and (iii) iitentcoonditoned successor features (Barreto et al., 2017) F, that encode the expected discounted sum of latent task features under the corresponding policy Tz. ie, F. E E (8))))))) mz]- We now explain how FB-CPR trains those components.
FB representations and FB-CPR, Among the different unsupervised RL approaches, forward- backward (FB) representations provide principled unsupervised training objective for jointly learn- ing latent task representations and their associated successor features. At a high level, FB learns finite-rank approximation of long-term policy dynamics, where B captures the low-frequency features that best summarize the long-range temporal dependencies between states. Formally, given a training state distribution p. the FB framework learns two mappings: a forward mapping F : S x A x Rd > Rd and backward mapping B : S - Rd such that the long-term transition dynamics induced by the policy π= decompose as:
(1) where for any region X c S of the state space, (s E X s, := E e X 8. a, π²) denotes the discounted visitation probabilities of reaching X under the policy Tz. starting from the state-action pair (s,a), a). Eq. I implies that F is the successor features of ((s) (B)) ))) (Touati et al., 2023). The learned representation & defines latent task space by inducing family of linear reward functions of the form, i.e., r=())= (2)))()))))))) (s))T..
M M (s ~ a(), ((s',,,)TBBsss)p(dddd)
In particular, each policy n, is optimized to maximize EE, (s))
T=] = a()),,=)"
ie., F(s, a, 2) = is a Q-value function of T. with reward r = Tz. Intuitively, 2 E 2 defines a task-centric latent space associated with the task feature o, where for each z, the corresponding ne optimizes the linear combination of Φ, r= = o 2. As shown in Section 3.4, the Z space learned by BFM-Zero is smooth and semantic, and it enables both zero-shot inference and few-shot adap- tation. Importantly, in contrast to standard RI. approaches, the set of reward functions of interest {r=) is not given (e.g., motion tracking) but learned, and it can represent wide range of tasks. FB- CPR (Tirinzoni et al., 2025) extends the general FB framework by introducing discriminator to regularize the unsupervised learning process to produce policies that are close to a set of demonstrated behaviors in a motion dataset M. Furthermore, while FB algorithm is offline, FB-CPR trained fully online and off- off- policy and does not require full-coverage offline dataset.
conditocodd
2.2 BFM-ZERO PRE-TRAINING FOR HUMANOID CONTROL
Before proceeding with the description of implementation details, we identify several design choices that are crucial for achieving sim-to-real transfer in unsupervised RI.
A) Asymmetric Training. To bridge the gap between simulation (full state) and real robot (partial ob- servability), we train the policy on observation history Or. while critics have access to privileged information (Ot,H1,s)) This setup improves policy robustness under limited sensing while leverag- ing privileged critics to provide accurate value estimates. Using history narrows the information gap between proprioceptive actors and privileged crities and improves adaptability under DR.
B) Scaling up to Massively Parallel Environments. Inspired by recent work on large- batch off policy RL (Seo et al., 2025), we scale training across thousands of environments with large replay buffers and high -o-ate-to-data (UTD) ratios. This enables efficient unsupervised training of a diverse family of policies while retaining stability, crucial step for scaling humanoid pretraining.
C) Domain Randomization (DR). To enhance robustness and adaptability, we randomize key physi- cal parameters (link masses, friction coefficients, joint offsets, torso center-of-mase and apply per-
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turbations and sensor noise. This prevents overfitting to simulation dynamics and ensures that poli- cies remain stable when deployed on real hardware (see Fig. 4 in Appendix).
D) Reward Regularization. In robotics (c.g., Hc et al., 2025a; Zakka et al., 2025), it is common to incorporate reward regularization techniques to avoid undesirable behaviors. For example, reaching the limit of the joint may lead to highly nonlinear behaviors that are difficult model in simulation or even damage the robot's hardware.
We train BFM-Zero within an off-policy actor-critic scheme. The volicy-conditional history- based, privileged forward map F and privileged backward map B are trained to minimize the temporal difference loss derived from the Bellman equation for successor measures (Touati & OI- livier, 2021). Let D the replay buffer of online interactions with the simulator and v is an arbitrary distribution over Z, we consider the following FB objective:
C(F, B) = E ( (OL.H $1, at, T B(o .s) FF(ooF $41,,0-1: 2) Blo s+)
2 F(o,, St, ar 2) S(11))
where: 2 ~ v. (Or.H. St, at, 0++1,11 14+1) ~ D, a1+1 = T(0 and (o*, ~ D. F and B denote the stop-gradient operator.
The auxiliary history-based. privileged critic QR that imposes safety and physical feasibility con- straints by incorporating Naux penalty rewards is learned with a standard Bellman residual loss:
Naux (x(#)) -0 8641, (0+1,0+11
C(Qn)= I. i (6
(Qn(o.H,St,
Finally, we employ the history-bused, privileged discriminator critic QD that grounds the unsu- pervised training toward human-like behaviors by assigning rewards based on a latent-conditioned discriminator. This acts both style regularization cess. As in (Tirinzoni et al., 2025), we employ variational representation of the Jensen-Shanne divergence and train the discriminator D with a GAN-style objective:
well bias in the online exploration pro-
C(D) -- -,,,,,, [log( (o, 8, z+))] ((o,.,))>D [log(1 D(o, 8, s))]
1[7) E(an)er s)) is zero-shot imitation embedding of the motion т. We can then fit style critic QD with Bellman residual loss similar to the auxiliary critic with a reward ((o..
where 2, 1
ra(or, $1, = 1- ())
Bringing together these critiques results in the final actor loss.
[(T) -EE (L) a,=x(0,,,,)),0m
F(O.. St, at, 2) ++ 2+Ap Q St, ar, 2) ARQRRRR(O.OH. St, ac 2)
Zero-shot inference. At test time, BFM-Zero can be used to solve different tasks in zero-shot fashion, i.e., without performing additional task- sask-spec learning, planning, or fine-tuning. Given an arbitrary reward function r(s), the corresponding Q function of π; can be formulated as
O. (s. = M (ds's (ds')s, (s, = E B( ((s')n(s'))
Since F(s, 2) is the Q function of we have = Exe B(s)r(s)). In practice, we where E D and D {(s,, r:)} is obtained by subsampling the online replay buffer. For a goal-reaching task, we have Zg B(sg). Finally, for tracking a motion T = (81,..., 8n), a sequence of policies { is obtained as 2, E B(e)), where H is a look-ahead horizon (Pirotta et al., 2024).
can leverage sample-based estimate, given by 2, 1
N E, r(s;)
(ss) B(s)
Few-Shot Adaptation. We can leverage optimization techniques for adaptation in latent space Z us- ing online interaction with the simulator test time. We demonstrate this by refining a static pose or an entire motion maximize [=2) E ('mns(s)) aR Σ Ek (x(oi, St, at)) For single-pose adaptation, we use the zero-shot policy 2g B(sg, 0g) as initial point and apply the Cross-Entropy Method (CEM) (Rubinstein, 1999; Rubinstein & Kroese, 2004). For trajectory we warm-start from a tracked motion sequence and perform zero-order, sampling-bar based trajectory optimization over a sequence of latent prompts, Z- using a dual-loop annealing schedule in the spirit of DIAL-MPC (Xue et al., 2025). This procedure consistently stabilizes challenging segments and reduces motion motion-tracking error, while retaining the human-like prior given by the dis- criminator without finetuning networks.
adaptation,
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move-ego-D-0
Model
Test env.
Test data
Track
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Mujaco (DR)
AMASS
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Figure 3: Tracking, reward, and goal-reaching performance across models for different testing configurations (left), and example distributions of reward evaluation scores for BFM-Zero in Isaac (DR) (right). Each metric is averaged over tasks. We consider the average return episodes lasting 500 steps for reward, the average joint position error Empipe averaged over the whole motion for tracking, and error Empipe averaged over the episode for goal-reaching.
EXPERIMENTS
In this section, we thoroughly evaluate BFM-Zero both in simulation and in real. We train BFM-Zero in simulated version of Unitree GI using IsaacLab (Mittal et al., 2023) at 200 Hz, while the control frequency 50 Hz. For the behavior dataset, we use the LAFANI dataset (Harvey et al., 2020) retargeted to the Unitree G1 robot. The LAFANI dataset contains 40 several-minute- long motions. We also demonstrate generality of BFM-Zero on a Booster T1 humanoid (App. ??).
3.1 ZERO-SHOT VALIDATION IN SIMULATION
In this section, we quantitatively assess the performance and robustness of BFM- Zero along differ- dimensions in simulation.
Asymmetric learning and domain randomization. We consider a privileged version of BFM-Zero where all components of the algorithm receive privileged information. We train this model in a simulated environment with nominal dynamical parameters (No DR) and we test it in the very same configuration. This serves unsupervised RL was previously shown to work (Tirinzoni et al.. 2025), although it leads to a model that not deployable on the real robot. We then compare to BFM-Zero trained and tested on domain randomized version of the environment (Sim DR), which corresponds to the model ac- tually deployed on the real robot. Overall, BFM-Zero 2.47%, 25.86%, 10.65% worse than BFM-Zero-pris across tracking, reward, and pose reaching tasks. This shows that despite the al- gorithmic changes made in BFM-Zero compared to FB-CPR, the learning dynamics is still correct and the model retains satisfactory performance compared its idealized version. Interestingly, reward tasks suffer from a larger drop in performance. This in part due to the sparse nature of consider, which makes them less forgiving
idcalized configuration similar to the problems where
the reward functions amplify any model error. We also conjecture that this may be related to the reward inference pro- cess with domain randomized data. In Fig. we also show the distribution of the performance of BFM-Zero for two representative reward functions across repetitions of the inference process' and episodes. While for move-ego-0.3 the performance is fairly consistent, for move-ego-0. O, we notice that few instances obtained very poor performance. We conjecture that this is related to the increased randomness of the data observed during training due to domain randomization, which makes inference with small subsampled dataset more brittle and prone to failure.
suboptimal behaviors and
Sim-to-sim performance. We evaluate the robustness of BFM-Zero to the dynamics of the hu- manoid by testing it in Mujoco. We notice that performance difference is limited (i.e., all variations are less than 7%), showing that the domain randomization at training and the history components in the actor and critics contribute good level of robustness and adaptivity.
-iitt--iistribution tasks. Finally, we evaluate BFM-Zero on a different set tracking and pose reaching tasks obtained from the AMASS dataset (Mahmood et al., 2019). We consider 175 out-of- distribution motions from the CMU subset of the AMASS and 10 manually-selectee poses from the motions in the entire AMASS dataset. We tests in Mujoco combine different dynamics and
In the reward inference, use dataset of states randomly subsampled from the training dataset. As result, multiple repetitions of the pracess may return different policies.

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Recovery from Unexpected Falls
Figure 4: Real-World Validation of Tracking. Left: Highly dynamic dancing. Middle: Frequently turning during walking. Right: Naturally recover continue track the motion.
in the air pose
Discontinous Goal Pose
Real Deploy Trajectory
Real- World Validation of Goal Reaching. (a) Continuously goal-reaching: the blue/yellow pose denotes the goal pose, while black marks the real robot pose, and gray visualizes the transition between each pose. (b) Transition from any pose to T-pose.
Figure 5:
distiitibbttiin tasks. While direct comparison of performance between LAFANI and AMASS tasks may be misleading due to the specific nature of the motions and poses used in the evaluation, we notice that overall BeM-Zero is able to successfully generalize and complete tracking and pose reaching even when exposed to tasks that are not represented in the training data.
3.2 ZERO-SHOT REAL-WORLD VALIDATION
Finally, we deploy the BFM-Zero model zero-shot on real Unitree G1 robot. In real-world val- idation, aim to 1) qualitatively confirm the model's tracking, reward optimization, and goal reaching capabilities on a few selected tasks; 2) assess its robustness to perturbations and failures (e.g., falling). All results in this section come from one model.
Tracking As shown in Fig. 1, 4, BFM-Zero enables the robot to track diverse motions, includ- ing various walking styles, highly dynamic dances, fighting, and sports. Even when unstable or falling (Right), it demonstrates remarkably gentle, natural, and safe behavior while recovering and continues tracking seamlessly. This capability stems not merely from robustness gained through dis- turbance training, but mostly from 7D-based off off-policy training and the use of a GAN-base reward which explicitly encourages human-likeness and regularization terms that enable it to draw upon a rich skill library-much like human-to adaptively complete the tracking task. Additionally, to evaluate the coverage and generalization capability, we used real videos and retargeted them to the GI. Despite the suboptimal motion quality and discontinuities introduced by occlusions of monoc- ular videos and artifacts in video estimation, the system is robust to lower quality data and can still successfully track these motions.
Goal Reaching For the goal-reaching task, we extract sequence of target poses by randomly sam- pling goal states and discarding their velocity components. The zero-shot latent of these poses are then permuted and sequentially provided to the policy. As illustrated in Fig. 5, the robot consistently converges to a natural configuration that closely approximates the target pose, even when the target infeasible (the Yellow in Fig. 5). Moreover, the resulting trajectory exhibits smooth and natu- ral transitions, whether between successive targets(5.a) or from an arbitrary pose to the T-pose(5.br (5.b), without the need for explicit interpolation, demonstrating the smoothness and high coverage of the learned skill space.
Reward Optimization We evaluate reward optimization in the real world with three task families: (i) locomotion rewards that specify base velocities and angular velocities, (ii) arm-movement re- wards that command wrist height, and (iii) pelvis-height rewards that request sitting, crouching.
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on the ground
Close in height
@A
-25cm
[e)
(e)
Rewards:
base-height
0/25cm
base-height ght 60cx forward
right-wirst
1m
wirsts ls backward/stde
Figure 6: Real-Woor World Reward Optimization. The red arrow represents the base velocity tracking target. (a)
sittings (b) crouch-0 0. 022;; (c) nove-low0. www0.6---- 6-ego-00 000 7; (d) Diverse hehaviors from one reward ra sea rm-m--1--------m------------ 1; (e) combing rai searm-m- with mwvemeoveeeeee eeo- 1opo-110 80-0.. 0.3 3 and move loove-ego-se 90-0.
(b)
(c)
Figure 7: Disturbance Rejection: (a) Keeps steady when kicked in the leg. (b) Absorbs a hard push with one smooth rear step. (c) Naturally stands up and returns to T-pose after heing yanked down.
or low-movement (Fig. 6(a-c)); reward definitions in Appendix C. With simple reward definitions, the robot faithfully executes base-height, base-velocity, and arm-movement commands. Composite skills can be derived from simply linear combination of the rewards (e.g., going backward while raising arms), demonstrating controllable, skill-level interpolability. Also, given specific reward, averaging over different resents a diverse collection potential optimal modes. (Fig. 6(d)). Formulating objectives through reward functions makes our policy intuitive for human users and receptive to language prompts.
from the replay buffer yields a set of latent variables that rep-
Disturbance Rejection One notable advantage of our policy its strong compliance and robust- ness. As illustrated in Fig. and 7, our framework enables the robot to withstand severe distur- bances-such as fierce pushes, kicks, or even being dragged to the ground, while recovering natural, human-like manner. For instance, after a strong forward shove, the robot instinctively closes its arms, takes several rapid steps in running-like pose, and then gradually slows down before re- opening its arms (Fig. 1). This level of robustness goes beyond the typical demonstrations seen in previous works: rather than fiercely reacting to the disturbances, our policy autonomously adapts. Although it receives only single latent vector from the static T-pose input, it can automatically deviate from the reference posture, adopt dynamic recovery pose, and eventually return to tracking the original T-pose just as a human would.
3.3 EFFICIENT ADAPTATION FOR BFM-ZERO
In this section we show how we leverage adaptation to improve the zero-shot inference performance.
Single Pose Adaptation We validate few-shot single-pose adaptation on hardware with an ad- ditional 4 mass rigidly attached to the torso link. Starting from the zero-shot latent iii we apply CEM to obtain 2", augmenting the rollout objective with a sparse task term r 1 Aega 200.5 A no-contact): which encourages right-foot clearance while avoiding unintended tacts. As shown in Fig. 8 (a), without adaptation, the motion driven by iii destabilizes and produces an environmental collision within 5s. In contrast, the optimized prompt =* maintains single-leg bal- ance for over 15s. These results indicate that prompt-level optimization alone can compensate for the aayload-inducee dynamics shift, without retuning network parameters.
Trajectory Adaptation For trajectory adaptation, we focus on optimizing a leaping motion under altered ground friction. We perform dual-annealing trajectory optimization (Xue et al., 2025) using the explicit tracking reward defined in (Luo et al., 2023). We used sampling with particle count N 2048, temperature schedules 31 0.85 and 32 0.9, and optimization iterations M 6. The reward curve and before/after adaptation key-point tracking performance is shown in Fig. 8(b), showing that our method significantly improves tracking accuracy, reducing error by 29.1 1%.
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Collision
Keep single leg 15s
0.0000
Tracking Reward
0.0675
0.0830
lkg
0.0800
objective
0.0075
Original Smoothed
0.0030
())
Before Adaptation
Adaptation
(b)
Ilorations
Figure 8: Few-Shot Adaptation: (a) Single-por pose adaptation improving single-leg standing under an additional payload. (b) Trajectory adaptation reduces tracking error.
Stanchiick
Reward
(a) Tracking trajectories segment
Jinpp
the latent space (2D).
(b) Representative latents (3D).
(c) Interpolation visualization
Figure 9: Latent space visualization and analysis.
3.4 THE LATENT SPACE STRUCTURE OF BFM- ZERO
As mentioned in Sect. 2.1, Zero provides an interpretable and structured representation of the behaviors of humanoid robot. This representation not only facilitates understanding of the policy space but also enables instantaneous interpolation of existing skills without retraining.
Visualizing the Latent Space To examine the structure of the latent space, we sample latent vector trajectories and project them onto a two-dimensional plane (Fig. 9a) to visualize the space, and also use three-dimensional sphere to present representative latent generated for tracking, reward optimization and goal reaching(Fig. 9b) using (-SNE (van der Maaten & Hinton, 2008). We can see the latent space is organized by motion style: semantically similar trajectories cluster, revealing shared task centric structure.
Motion Interpolation on the Latent Space The structured nature enables smooth interpolation between latent representations. We can leverage Spherical Linear Interpolation (Jafari & Molaci, 2014) to generate intermediate latent vectors along the geodesic are between the two end-points. To evaluate interpolated behaviors, we feed the resulting in-between =r=0.5 into the BEM-Zero policy, and deploy it on both simulated and real humanoid robots. As shown Fig. 9c, the interpolated policy produces semantically meaningful intermediate skills in zero-shot manner. These behaviors compose immediately- no -no additional training required.
DISCUSSION
In this paper we showed the first time that off-policy unsupervised RL viable approach to train a behavioral foundation model for whole-body control of a real humanoid robot. While BFM-Zero shows remarkable level of generalization and robustness, it still suffers from several limitations: 1) The scope and performance of the behaviors expressed by BFM-Zero is connected to the motions used training. Investigating the connection between the size of motion datasets, simulated datasets, architecture and model performance (e.g., quantity and quality of the learndd behaviors) and consolidating it into scaling laws is important to guide future iterations of this ap- proach. 2) While history-base -based actor and critics and domain randomization reduced the sim-to-real

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gap, believe algorithms with better online adaptation capabilities needed to reliably express more complex movements. 3) While we performed preliminary investigation of test-time adapta- tion, more thorough understanding of fast adaptation and fine-tuning of these models is needed to broaden their practical applicability.
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Appendix
A Related Work
14
Training details
15
B.I BFM-Zero
15
C Tasks and Metries
18
D Use of Large Language Models
20
E Application on Booster T1
21
More Ablations
21
F1 Model and Data Ablations for Tracking and Reward
F.2 DR and Aux Rewards
25
F3 Ablation on Z dimension
26
G Quantitative Real-world Validation
27
H Tracking Performance Comparison
27
I Latent space investigation
28
A RELATED WORK
In recent years, learning-based methods have made sigmificant progress in whole-body control for humanoid robots. The largest body of work has focused on simulated humanoids. While these meth- ods have demonstrated impressive capabilities in generating complex and dynamic behaviors using reinforcement learning (Peng et al., 2018; Luo et al., 2023; 2024; Tessler et al., 2024; Serifi et al., 2024), sim-to-real transfer remains critical challenge in deploying learned policies on real- world humanoid robots. Various strategies have been proposed to bridge this gap, including domain ran- domization, system identification, asymmetric training, etc. However, the majority of these methods focus single-tasi learning, where a policy is trained perform specific task, such as walking, running and get up (Radosavovic et al., 2024a;b; Chen et al., 2024; Sco et al., 2025; Zakka et al., 2025; He al., 2025c; Peng et al., 2017).
Recently, mostly 2025, there has been a surge of interest in developing multi-task and generalist humanoid control policies that can perform wide range of tasks (He al., 2024b; 2025a; Zhang et al., 2025; Zeng et al., 2025; Yin et al., 2025; Chen et al., 2025; He et al., 2024a; 2025b; Cheng al., 2024; Dugar al., 2025; Fu al., 2024). The majority of these methods builds top of approaches developed for simulated humanoids, and enhance them to be robust enough for sim- to-real transfer. While ASAP (He et al., 2025a) pre-train motion tracking policies in simulation and deploy them on the real robot to collect data to train a delta (residual) action model, the most common approach is first train a motion tracking policy (or multiple policies) in simulation, and then distill it into single multi-task policy that can perform all the skills in the motion dataset. Common approaches for distillation include using a conditional variational autoencoder to learn a latent space of skills and doing online distillation (He et al., 2024b; Yin et al., 2025; Zeng et al., 2025; Chen et al., 2025; Zhang et al., 2025) or using diffusion models (Liao et al., 2025). However, all these methods require two stages of training to enable promptable policies, they inherently limited by the quality of the motion since the base policies are trained to track the motion, and they relay on-policy RI. algorithms. Our method represents significant departure from this paradigm
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by directly learning promptable multi-task policy using off off-po RL algorithm, which offer a much more reach and structured space of skills, and is not limited by the quality of the motion dataset.
B TRAINING DETAILS
The agent interacts with the environment via episodes of fix length T = 500 steps. The algorithm has access to the dataset M containing observation-only motions. Similarly to (Tirinzoni et al,, 2025), the initial state distribution of an episode is mixture between randomly generated falling positions and states in MM (motion initialization). We use prioritization to sample motions from M and, inside motion, the state is uniformly sampled. We use exponential prioritization scheme based on the agent's ability to track motion. To have more fine-grained prioritization. we split the 40 LAFANI (Harvey al., 2020) motions into chunks of 10 seconds. Every Neval interaction steps, we evaluate all the motions and update the priorities base the earth mover's distance (Rubner al., 2000, EMD). For each motion m E M,, the priority given by
max 0.5; min (EMD(m)))) }:4
p(m) x 2
We take inspiration from the recipe in FastTD3 (Seo et al., 2025) to scale up unsupervised off-policy RL to using massively parallel environments. We standard MLPs for all the components of the model, even for handling history. We simulate Newv parallel (and independent) environments at each step. We scale the buffer size accordingly the number of environments, following the rule Nhutfer x New x T. We use batch size of Nhatch and we use an update-to-data ratio of Nups gradient steps per (parallel) environment step. We train the model for total number of environment steps Nerain Ngrad Neax We report the value of these parameters in Tab. ??, the missing parameters Nap-
are as in (Tirinzoni et al., 2025).
Network architectures. We residual architecture for actor and critics (F, Qp) with blocks akin to those of transformer architectures (Vaswani et al., 2017), involving residual connections, layer normalization, and Mish activation functions (Misra, 2020). We a simple MLP for the critic Q n. We use an ensemble composed of two networks for critics. For discriminator and backward map we use a standard MLP with ReLu activation. Refer Tab. for more details.
B. BFM-ZERO
We provide here sketch of BFM-Zero in (Alg. 1). We report the algorithm without parallel networks for clarity. for clarity as well, we report the FB loss here. Let at ~ π (2/, 24) where 2r; (oi,1, 8;), then
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Parameter
Value
Environment and Training Setup
History Length H
Episode Length
500
Newv
1024
batch
1024
Nups
16
Ngrad
3M
Ntrain
192M
Nbuffer
10
Neval
N Narain/ /2
Buffer Size (transitions)
5M
Discount Factor
0.98
Number of Seeding Steps
10 Nonv
Fall Initialization Probability
0.3
Learning and Regularization
Sequence Length (Trajectory Sampling)
Latent Dimension d Discriminator Reg. Coef.
256
0.05
Reward Reg. Coef. on
0.02
Gradient Penalty
10
Learning Rate F
3 10
Learning Rate B
10
Learning Rate D
10
Learning Rate Actor
3 10
Learning Rate Q
3-
Learning Rate Q&
3 3-
Orthonormality Loss Coefficient
100
Inference
Number of samples for reward inference Tracking look ahead in sim Tracking look ahead in real
400000
Seq. length (real)
Table 1: Train parameters
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embedding
Critis
Output volue (692))
Action distribtiin
Durgur value
output
Sal oor
Normal
Forward
Uneer
Lagae ELRIT
Layer normh
Adin
Add
Nx
Forward
Vish
Nx
Nx
Layernormy norm
Food Forwatc
Grosnl
Disorimhattor
Laye (orm
Mish
Ouspun value
Concalionato
Layel norm
Fipil emoedd
Fipu: artasding
rnput entasliing
Irpal
Free
Input
Table Visual representation of the network architectures.
Hyperparameter
Critics (F, Qo, QR)
Actor
Discriminator
B
Input Variables
(I. a, 2) F: d. QD. Qu:
(x,=)
(a,z)
(x)
Output Dim
29
1
d
Observation Variable
(0,, 8t)
(st, 0L)
(st, 01)
Embedding Residual Blocks
Embedding Hidden Units
2048
2048
Residual Blocks
6
6
Feed Forward Hidden Layers
1
2
1 256
Feed Forward Hidden Units
2048
2048
1024
Activations
Mish 2
Mish
ReLU
ReLU 1 0.2M
Number of Parallel Networks
Num. Parameters (no target)
135.8M, QD. Qn: 134.8M
31.9M
2.9M
Total Parameters
440.5M
Table 3: Network architecture parameters used for real tests. 8t is the privileged information and or is the proprioceptive information. Or,H (or- H, at- H,...,00 0z3 denotes the history of proprioceptive states and actions.
Domain Randomization
Additive Observation Noise
Regularization Rewards
Parameter
Range
Observation
Range
Name
Weight
COM Offiset [m]
2(([0.02, 0.02))
q: 9
4([-0.01, 0.01])
DoF Limit
10
Link Mass
21([0.95,1.08
i
u[[-0.5, 0.5])
Action Rate
Friction
U([-0.5, 1.25)
grav,
4([-0.05, 0.05)
Self Contact
Default Joint Pos [m]
u([-0.02, 0.02))
X[[-0.05.0.05
Feet Orientation
-0.4
Push Robots [m/s]
O([0,
Ankle Roll
-4
Feet Slip
22
Table 4: Additional training configurations.
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fn 2n(n - 1) iph
Flri, ais B(ck, -F((d,,), B((s df))
Σ
F F(ris ai, B((d,
(2)
o() (ooo))BB((B))) (((s,.)))(
Σ
B(s [(((,o))BBB(,,)))
2n(n 1)
" iE(m]
(((i, 21) 2i)
B( o
FF 24)
iE(n]
Algorithm BFM-Zero Pre-Training
I: Initialize empty train buffer: Dontine + 0
2: Initialize expert buffer M with action-free trajectorics
3: for ? 1,... do
4: //Odinee internction
5: Sample
Newe RoNo (if needed)
6:
Execute a; ~ TO,, 2:) E R. Newv X in the simulated environments
7: Store (#t, o', f s,,, 0 1,1, H. z6) in Donline
8:
MUpdate
for j I..... Nupa do Sample batch of Nbatch transitions {oo, , 8t, a, o.H 84, k)) from Donline
10:
11:
Sample batch of Tacq sequences {[W5,1 W3,2 ... Wg 13 from M where (st,
Taey
12:
//Encode expert and updute discriminator
+ Tooq Foen B(W5,a): :2 f- Vd T2
13:
1 Teea log D(wy, 24) Ei log(1 D(s,,00,22)
Ediecriminator
15:
/Upulate representation E and B that =(),a;=) B(x)) M (ds'ls,)
16:
Refer Eq. 2
/hote that D does not use history Compute discriminator reward: rp + log(D(s Oc, 2)) log(1 ((s,, Vi E in]
18:
19:
Let x; (O.,H, s:) and sample u, ~ x (0.HH, 2.) for all i [n]. Then
20:
Loritiep Eiejn| (Qn( di, 2;) - r?? 1m 2))
21:
PeritieR E)e()) (Qn(I;, ai 2:) Σ+ r (x() 12
22:
Lactor = - - F(a; us, 2;) z, anQp(ur us z)) + anQn(es, us
23:
/Updaate target networks
24:
end for
25:
end for
C TASKS AND METRICS
In this section we provide a complete description of the tasks and metrics.
Goal-based evaluation We have manually extracted 21 "stable" poses (i.c., states with zero ve- locities) from the train dataset (i.e., LAFANI) and 10 poses from the test dataset (i.c., AMASS). We report the selected poses from LAFAN1 in Fig 10. To evaluate how close the agent to the goal pose, the joint error defined following
Empipe (e, (e,g)
(( -(9))12
where e is am episode and q is the joint position (i.c., 29D). We report the average across goals. Episodes are of fix length H 500.
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Figure 10: Goal poses selected from frames of the LAFANI dataset (Harvey et al., 2020) (top) and AMASS dataset (Mahmood al., 2019) (bottom).
Tracking evaluation This evaluation aims to assess the ability of the model imitate sequence of poses, by ideally matching both positions and velocities. We evaluate the agent both on the train dataset (i.e., LAFANI) and out-of-distribution motions selected from AMASS (retargeted GI). In particular, we randomly selected 175 motions from the CMU dataset of AMASS. For evaluation, we use the same metric in goal evaluation, i.e..
le] (1 (m))2
Empipe (e, (e,m)
and report the average across motions.
Reward evaluation We define 6 categories of rewards inspired by (Tirinzoni et al., 2025). Re- wards are function of the next state and normalized [0, 1].
Standing. We evaluate the ability of the agent to stand with the pelvis at different heights. move-ego-O-0 requires pelvis above 60cm and zero velocity, while move- low 5-0-0 requires the pelvis to be between 50cm and 65cm.
Locomotion. This category includes rewards related that requires the agent to move at a certain speed, in a certain direction and at certain height. We consider representative rewards (move-ego-0-0.77 move-ego-90-0.77 move-ego--90-0.. move-ego-0-0.3 move-ego-180-0.3 which include forward, lateral and backward movement. We additionally test also walking forward but with the pelvis at low height (move-ego-low0. 6-0-0.7).
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Rotation. We require the robot to rotate along the vertical axis (i.e., while standing). We consider rotating clockwise and counter clockwise (i.e., rotate-2-
and cotttt-2--5--1 5).
Ground poses. To further stress the ability of the model to control the vertical position, define rewards requiring the agent to sit on the ground (sitting) or having the pelvis slightly above the ground (crouch-0.. 25 is about 25cm above the ground).
Arm raise. We require the robot to stand in a steady position and to reach certain vertical posi- tion with the arms (measured at the wrists). We consider low (a E [0.6m, 0.8m]) and medium (= > 1m) positions for the wrists, with soft margins (rai sraissemms-1-- raisearms 1-m, ra meisearms-m-s ra searmss searms-m-m).
Combined rewards. We finally evaluate the ability the agent to maximize rewards that require combining multiple skills. In particular, we test combinations of locomotion and rotation with arm movements. We selected 8 combinations of rewards.
Overall, tested 24 rewards and evaluted perfomance via the cumulative return over episodes of T = 500 steps. The initial state of an episode is the default pose.
D USE OF LARGE LANGUAGE MODELS
The authors use LLM tools for refining the expression in the final draft of the paper, but did not use for idea generation, training, and other experiments.
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E APPLICATION ON BOOSTER T1
We additionally evaluate the generality of our framework by testing BEM-Zero on Booster Tl humanoid robot. The LAFANI dataset retargeted to T1 using LocoMujoco (Al-Hafez et al., 2023) and we train the policy with exact same generalization ability, allowing TI also perform natural walking and expressive dancing motions, as shown in Figure lla.
as G1. The algorithm shows strong
(a) Qualitative results simulation.
Discriminator Reward
EMD distance (LAFAN1)
504
1004
1004
2009
300
Training Curves.
Figure 11: Applications of BFM Zero on Booster T1.
To demonstrate convergence, we report the training curves (Figure 11b) of the discriminator rewards together with the validation EMD distance over the course of training. The curves show rapid convergence. (Note: lower discriminator rewards indicate that the policy more effectively deceives the discriminator.)
F MORE ABLATIONS
F.1 MODEL AND DATA ABLATIONS FOR TRACKING AND REWARD
We perform ablations on both the data and model size. For training the model in the main paper, we used only the LAFANI dataset (Harvey et al., 2020). In these ablations, we additionally lever- age motions from the CMU and BMLHandball subsets of AMASS (Mahmood et al., 2019). We selected these two sub-datasets for computation/tine reasons and because we believe they contains sufficiently different behaviors than the one in LAFANI. We consider individual datasets (referred to LAFANI and AMASS in the figures), well as datasets obtained by merging X percent of the two datasets (with X (12.5% 25% 50%, 75%, 100%)).
We consider different network architectures, including simple feed-forward networks and residual architectures with a varying number of blocks (see Table 6).
We evaluate tracking and reward inferece. For tracking, use the same test dataset as in (Tirinzoni et al., 2025), but we removed motions from CMU and BMnHaadball to ensure complete separation from the training datasets. For reward inference,
600, 000 samples from the LAFAN1 motion
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Model: MLP
Model: ResNet
Thacking Inference on test Eneqs: (1))
0.9
0.9
0.7
40M
55M
121H
160M
0.7
60M
120M
1909M
441M
6/80M
Reward Inference from LAHO1
250
250
Return (1)
200
200
150
150
100
40M
S3M
12IH
180M
100
60M
120M
180
441M
6808
Number of parameters
Number of parameters
100% mix 9.0h
25% mix 2.2h
75% mix 6.7h
LAFAN1 2.4h
5% mix 1.1h
50% mix 4.5h
AMASS 6.5h
Figure 12: Tracking and reward performance the test set for different models and datasets. The lower the better for tracking and the higher the better for reward. Reward inference is done using 600, 000 samples from the LAFANI expert buffer.
Dataset LAFANI
Frames 30FPPS
Duration
258600
2.4h
AMASS
698437
6.5h
12.5%
119620
1.1h
25%
234982
2.2h
50%
475793
4.4h
75%
721720
6.7h
100%
957037
8.9h
Table 5: Number of frames per dataset
dataset and repeated the inference 10 batches. This is different what done in the main paper where we used the replay buffer. We refer the reader to the paragraph below for detailed discussion.
Number of Parameters
Architecture
Model
QR
B
Q
Total
ResNet
3-block, 2048dim
19.3M
59.2M
20Tk
59.2M
2.9M
60.3M
201.IM
ResNet*
6-block, 2048dim
31.9M
134.8M
201k
134.8M
2.9M
135.9M
440.5M
ResNet
9-block, 2048dim
44.5M
210.4M
201k
210.4M
2.9M
211.5M
679.9M
ResNet
3-block, 1024dim
5.5M
17.0M
201k
17.0M
2.9M
17.6M
60.2M
ResNet
6-block, 1024dim
8.6M
36.0M
201k
36.0M
2.9M
36.5M
120.1M
ResNet
9-block, 1024dim
11.8M
54,9M
201k
54.9M
2.9M
55.4M
180.1M
MLP
2-layer, 1024dim
4.4M
10.7M
201k
10.7M
2.9M
11.2M
40.
MLP
2-layer, 2048dim
15.1M
34.0M
201k
34.0M
2.9M
35.0M
121 LM
MLP
4-yyer, 1024dim
6.5M
14.9M
201k
14.9M
2.9M
15.4M
54.8M
MLP
layer, 2048dim
23.5M
50.8M
201k
50.8M
2.9M
51.8M
179.9M
Table Configurations of the architectures and total number of parameters. denotes the configu- ration used in the main paper.
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Tracking. We report the results of our ablation in Figure 12 over single seed. As we increase the total capacity of the model, tracking performance improves for almost all of the mocap datasets used for regularization (see top line). When using LAFANI urates quite early. We believe this is because test motions, and despite being separated from any training motion set, likely much closer the motions in CMU and Handball than those in LAFANI. We can further notice that residual architectures achieve better performance w.r.t. sim- ple MLP architectures, and we can scale residual architectures to larger sizes. Furthermore, found training to be instable when scaling MLP to larger architectures (certain seeds diverged).
motion dataset, performance sat-
Reward. We observe a mild improvement trend for reward inference when increasing the model size. However, training with LAFANI (in some proportion) appears to be important in this case, as reward performance drops when we train only with the subset of AMASS. We also evaluated reward inference performance using both the training buffer (i.e., the replay buffer generated by the agent) and the training motion set that may differ from the LAFANI dataset. In both cases, the average performance decreases, with a much more significant drop when using the training buffer. We believe this may due to the fact that 1) samples in the buffer are collected with domain not randomized; 2) motions in LAFANI more
randomization, whereas the motion buffers aligned with the majority of tasks we evaluate. To validate this conjecture, we report Figure 14 the distribution of returns for each tasks for model trained using only LAFANI motions (6 block ResNet architecture with 2048 hidden dim) and reward inference done with LAFANI or the replay buffer. We can notice that inference with LAFANI leads better policies and thus returns on tasks covered in the dataset (c.g., moving forward, backward, on the side). However, we are able to infer better policies for "non-standard" tasks (e.g., rotate, crouching, move with low height) when using the experience self generated by the agent because these samples are collected by directly deploying policies learned by the agent. Selecting the optimal dataset for reward inference could be an interesting direction for future research.
Model: MLP
Model: ResNet
Reward inference
from train buffer Return (1)
250
250
200
200
150
150
100
20M
55M
121M
180M
100
6GM
120M
1881
441M
6804
parameters
Reward inference
from train motions
250
250
Retum (T)
200
200
150
150
100
40M
55M
121M
180M
100
60M
120M
1880
44M
680M
parameters
parameters
- 100% mix - 9.0h
25% mix - 2.2h
*
75% mix 6.7h
LAPAN1 2.4h
12.5%
AMASS
Figure 13: Reward inference performance when using the experience generated by the agent (i.e., online replay buffer) or the motion dataset used for training. We get better reward performance when using the motion dataset, in particular when using LAFANI (see Fig. 12).
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crouch-0
crouch-0.25
move-arms-0-0.7-4
move-arms-O-O. --0--rmm----07--mm
100
200
300
400
5000
200
300
400
5000
100
200
300
400
5000
100
200
400
500
move-arms-0-007-mm
move-arms-180-4 4-1-m
move-arms--80-0044-mm
100
200
300
400
5000
200
300
400
100
200
4.00
5909
100
200
300
500
90000-0
move-ego-0-0
move-ego-0-0.3
move-ego-0-0..
move-ego-180-0.3
move-ego-90-0.7
move-ego-low0.5-00g
mave-ega-law0.6-000
100
200
300
400
5000
100
200
300
400
5000
100
200
300
400
5000
100
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500
ralsearms-14
ralsearms-l-m
ralsearms- 5-m-m
rotate-z-5-0.5
200
900
400
5000
200
300
400
5000
100
400
5000
100
200
500
rotate-2-5-0.5
sitonground
spin-arms-5-H
spin-arms-5-tna
160 Cumulative Reward
Cumulative Reward
Cumulative Reward
Cumulative Reward
inference on lafan motions
inference on replay buffer
Figure 14: Return distributions for different tasks when using LAFANI motions the agent self- generated samples (replay buffer) for reward inference. Reward inference repeated 10 times leading to evaluating 10 different policies for each tasks. Each policy was evaluated over 20
episodes.
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6003
without aux reward
0.50
Fb Lozs
with aux reward
20000
Diec Lese
6.44
0.22
10003
20000
6.30
5.00
0.25 0.53 0.75 1.00 150
1.75 2.00
0.55
2.00
Environment Steps
Environment Steps
Environment Steps
Auxiliary Reward
Tracking (L)
Goal ())
Reward (LAFANI)
Yes
0.9806
0.9673
273.1
No
0.9572
0.9664
287.9
Figure 15: Examples of training losses function of the number of environment steps when training with and without auxiliary rewards (top). Bottom table shows the evaluation score of the models tracking, goal reaching and reward inference. The model is the ResNet model with blocks and 2048 hidden dimension.
140
1:00
without reward
Ponalty Undesired Contact (1 Penalty Ankle Roll
0.12
with reward
Penalty Feet
04-
0.30
0.00
0.4
Penalty
0.04
1.0
1.50
1.0
135
108
0.24
0.64
0.32
Limits Det Pos
Penalty Silppage
0.30
86-
0.09
0.-
0.54
2.20
0.00
0.50
6.75
Environment Steps
Environment
Steps
Environment
Steps
Figure 16: Auxiliary reward metrics as a function of the number of steps training. We can clearly that the model trained with auxiliary rewards optimizes the provided auxiliary tasks.
F.2 DR AND AUX REWARDS
The Domain Randomization (DR) and Reward Regularization (RR) terms are primarily informed by physical constraints and the sim-to-real gap. For example, penalize large actions because motors exhibit highly nonlinear behavior near joint limits a phenomenon that the simulator does not fully capture. In simulation, the impact of enabling disabling these components is difficult observe, as metrics and training outcomes remain very similar. However, on the real robot, these terms have significant effect, and their inclusion essential.
For example, removing auxiliary rewards causes the robot to become highly unstable in the real world, primarily for reasons: The robot tends to use large control inputs, which result in behaviors that are not accurately modeled by the simulator. 2) Foot placement becomes less precise than in simulation, leading to unstable poses. By inspecting the training curves related to auxiliary penalties, we can clearly see that these metrics (e.g., action rate and feet orientation) are optimized by the model using auxiliary rewards (see Figure 16). contrast, training curves and evaluation metrics in simulation (see Figure 15) remain very similar regardless of whether auxiliary rewards are included. In some cases, performance in simulation may even appear to improve when auxiliary rewards are removed (see table in Figure 15). However, this improvement does not translate to the real robot. The gap between simulation and reality complicates research iterations, as simulation metrics cannot always be relied upon to predict real-world performance.
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350
1.
325
2000
300
Vel Dist
3620
230
225
2.50
2.75
2.00
0.50 675
3.00
125
2000
Environment Stops
Enviionmmnt Steps
Environment Steps
32
128
256
384
512
1024
Figure 17: Evaluation curves for tracking during training as function of the steps in the envi- ronment for different latent space dimension (i.e., dz). These metries are computed on the motion dataset used for training (i.e., LAFANI). Results are averaged over 2 seeds.
330 COTVEEEEEE
Figure 18: Reward evaluation score split by individual tasks for models with different latent space dimension dz, along with the score increase after CEM adaptation on selected tasks.
F.3 ABLATION ON Z. DIMENSION
In this section, we investigate the impact of the dimensionality of the latent space Z on the model performance. The results the main paper corresponds to dimension 256, here we ablate the fol- lowing values de E {32, 64, 128, 256,384, 512, 1024]. We kept all the other training parameters fixed as in Table 3 and Table
Figure 17 reports the tracking performance the train motion dataset function of the number of steps in the environment. We can see that low z dimensions (32, 64, 128) lead to poor performance. Models with dimension of 256 more have similar performance. When moving the test evalu- ation (see table 7), we observe similar trend for all inference modalities; that is, the performance of models with dimensions of 256 or more is very similar. We do not observe any performance degra- dation that shows that the training is stable, we even observe an improvement in tracking and reward inference. Only when moving to dimension 1024 we observe a drop performance in reward infer- ence. This may due to the fact that the zero-shot inference becomes much more complicated in such large space.
To validate this, we adapt the zero-shot latent via CEM optimization on selected tasks, including ones where 1024 latent dimensional models struggled. We run CEM for iterations with popu- lation size of 1024, and sweep search space standard deviation over 0)01, 0.1, 0.5, 1.0, 5.0 and top- clites to keep over 1%, 2%, 5%, 10%. We repeat CEM per task three times. Then per task and model, report the best average CEM result over the parameter sweep. Figure 18 shows that model with z dimension 1024 has poor zero-shot performance some move ego tasks (e.g., move- and move-ego-180), but after CEM adaptation matches outperforms 32 and 256 dimensional models. This indicates that, indeed, zero-shot inference is more difficult at high dimensional latent space, but the model is still capable of reaching high performance with correct adaptation.
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Z dimension
Tracking (J)
Goal ())
Reward (LAFANI)
32
1.3963
1.6870
213.8224
1.2455
1.3141
240.4211
128
1.0512
1.0830
263,9501
0,9806
0,9673
273.0826
384
0.9266
0.9704
290.9922
512
0.9232
0.9758
307.3982
1024
0.8986
0.9875
252.3514
Table 7: Test metrics for the different inference modalities for models trained with different di- mensions. The base configuration has the one in the main paper (see Table and Table 1). Results averaged over seeds.
G QUANTITATIVE REAL-WORLD VALIDATION
To further evaluate the model's sim-to-real transferability, we choose several representative motions, measure their real-world tracking errors (Table 8), and compare these results with their simulation counterparts. sim-nodr corresponds to perfectly matched simulator with dynamics centered in the training range, whereas sim dr incorporates dynamic randomization.
Motions
Start Frame
End Frame
mpine (
ESLa--ndE (1)
Esim-dt (L)
Dance-1
2400
1.1945
0.7631
0.7736
Dance-2
2000
1.0003
0.9047
0.9235
Walk slowly
1
320
0,8662
0.5746
0.6561
Walk fast
4061
4285
1,2920
0.7421
0.7931
Walk and turn
2136
2870
1.1528
0.7481
0.7560
Marching
10931
11710
1.3989
0.9258
0.9220
Average
1.1408
0.7764
0.8041
Table 8: Comparison of real-world and simulation performance
We observe that the real-world tracking eтror is slightly larger, likely due to starting from a random initial pose in reality and the inherent sim-to-real gap. Overall, the discrepancy between simulation and real is only 0.01 17 rad per joint, which is acceptable and demonstrates robust simulation-to-real transfer.
H TRACKING PERFORMANCE COMPARISON
To clarify, BFM-Zero is, knowledge, the first to apply unsupervised RL to humanoid control capable of performing diverse tasks zero-shot. Prior frameworks typically rely on task-specific tracking rewards and focus purely single task: general motion tracking. In contrast, BFM-Zero a fundamentally different method that constructs unified latent space that captures reusable skills and supports reward optimization and discontinuous goal reaching. Nevertheless, we compared our method with the SOTA general motion tracking controller GMT Chen et al. (2025) in tracking performance. We their released checkpoint and test the test set in the MuJoCo. Using their released checkpoint, we evaluate Empipe on the test set in the same MuJoCo environment. As GMT Chen al. (2025) cannot naturally stand up, the metric is computed only over motion segments where the episode has not yet been terminated.
environment in
Tracking Error (Emupjpe (.)
Methods
Number of Samples (L)
LAFANI
AMASS
GMT
2.2425
1.9064
6800M
BFM-Zen Zero
1.0789
1.0342
200M
Table 9: Comparison of general tracking methods and BFM-Zero
In particular, we observe that GMT struggles with get-up motions and other on the-ground actions, significantly impairing its tracking performance the test Even in terms of tracking accuracy,
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Random Z rollouts
Dataset motions
Mero tracking EMD
Random init. tracking EMD
0.8
Donsity
0.4
0.0
3
Figure 19: Distribution of EMD distances between two rollouts from two randomly sampled latents.
our results are comparable to GMT Chen et al. (2025) (Table 9), which already outperforms Ex- body2 Cheng et al. (2024) and OmniH20 He al. (2025b) in their respective evaluations. More importantly, BFM-Zero clearly surpasses GMT in fully out-of-distribution scenarios-sur as ex- ternal pushes, falls, or random initializations BFM-Zero naturally regains stability and continues track robustly, due to its well-regularized, smooth, and dynamically aware skill space with even fewer samples.
GMT typically fails to recover. In contrast,
I LATENT SPACE INVESTIGATION
To study the latent space more quantitatively, we replicate latent space studies done in the original FB-CPR work (Tirinzoni al., 2025). Overall, these results indicate that, much like FB-CPR, BFM- Zero latent space contains an equally diverse set of motions as the training set, while covering the motions of the training set but also capturing motions not included in the training set. The latent space is clustered semantically by motion type, with clearly separated clusters.
Behavior diversity. We sample 10, 000 pairs of random latents (z's), roll them out for 500 steps (10 seconds), and calculate the EMD of the two rollouts. For perspective, also plot the distri- bution of EMDs between all the training motions, and include tracking evaluation performance as EMD on LAFANI dataset, both for randomly initialized policy and final version of the model. If the latent space maps only to a small set of motions, we expect to see small EMDs. As we see in Figure 19, the distributions of random rollouts and motions overlap with similar shape, indicating that BFM-Zero Zero latent space captures the diversity of motions of the training set. Some of the random motions are further apart than randomly initialized policy from training motions (EMD > 2.8, 8%), further indicating two randomly sampled latents can have very distinct motions.
Coverage of the training dataset We sample random 1000 random z's, roll them out for 500 steps, and calculate EMD to all the motions in the training LAFANI dataset (we the dataset split into 10s motions). We then label each motion in the dataset "covered" it has the smallest EMD to least one random rollout. We then loosen this criteria by adding progressively increasing margin m around each motion: if motion's 's distance to rollout is at most m larger than the distance to the closest motion, then motion counted as "covered". Ideally, randomly sampled motions should cover 100% of datasets in this way with small margin. In Figure 20, we see that 1000 random rollouts cover 26% of the 862 motions this way at zero margin, and by 0.7 margin all motions are covered. EMD of 0.7 is slightly higher than final tracking performance EMD (0.6) of the model, where the model is able to replicate most motions accurately. This result indicates that the model covers the dataset of motions. We also plot the distribution of the EMD the closest motion. Even the random samples cover all motions, they may be only replicating those specific motions (i.c., the latent space only covers the training motions). In Figure 20, see that the EMD distance spans
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135
1.0
130
9.8-
0.2
022
Theuhadd
CMD From rendom
dhoest moteeii Selaet
Figure 20: Coverage of the dataset by randomly sampling behavior latents, rolling them out and matching the trajectories with the closest motion (left), and the distribution of distances to the closest motions (right).
Dance
Fight
Fight sports
Jumps
Run
Sprint
Walk
Figure 21: UMAP dimensionality reduction of LAFANI motions embedded into BFM-Zero's latent space.
1.0 with mode at 1.5, which indicates that most randomly motions are significantly different from their closest match in the dataset, while some (EMD 2) being almost far the average distance of two randomly sampled motions.
Latent space clustering.
study the clustering of motions in the latent space, we embed the motions in the LAFANI dataset using ERFa (Tirinzoni et al., 2025) (embed every frame of the motion and then average over). In FB-CPR work, authors took only middle part of the motions, but here embed the whole motions they already separated into 10-second clips of the full motions. This will lead some points to be clustered close together (from the same motion), but also lead to potential overlap (e.g.. jumping and walking motions share overlapping sequences of person standing still). Figure shows the clustering of the points, with different regions for "fall and get up" and "dance". For quantitative numbers, create centroid embedding for each motion class by averaging the embeddings of that motion, and then assign new labels for each embedding based on which is the closest centroid. This mapping reaches 78.8% accuracy and 0.598 normalized mutual information (using normal szedizemmen peeedmuuuual finfo_s score function from scikit- learn package (Pedregosa al., 2)) which indicates of clustered latent space by motion groups.
29


Reviews:
[{"review_id": "3BJvpVU6PJ", "reviewer": "Reviewer_yhgB", "summary": "BFM-Zero introduces a framework to learn humanoid control from unsupervised RL. As opposed to most approaches that define motion tracking rewards over a dataset, BFM-Zero allows the model to explore in an unstructured way and pushes its action distribution toward human-like motions with a GAN-style discriminator trained on motion capture data. The authors demonstrate real world deployments of BFM-Zero.", "questions": "I'm curious to learn more about the latent space design decisions. How does the dimensionality affect the ease of prompting and performance of the model?\n\nDoes the model learn diverse behaviors or does it mode-collapse like on-policy algorithms?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method contrasts the dominant PPO over a motion dataset approach and demonstrates that it works. This may present a path forward to locomotion models trained with less reward engineering.\n- Smooth and structured latent space is likely helpful for multimodal prompting or other downstream tasks.\n- Pretraining seems scalable, subject to limits of the simulation model.\n-Real world sim-to-real deployment of off policy-trained model.", "weaknesses": "- \"Foundation model\" itself may be overclaiming since the model is essentially just a low level controller without rich vision or touch sensing.\n- Prompting occurs in a human-uninterpretable latent space rather than something like language.\n- More detailed comparisons of computational cost and representation quality with typical on-policy methods would be more convincing to validate the usefulness of such an approach.\n- Policy quality is still upper-bounded by simulation environment", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RlFPUFSFXq", "reviewer": "Reviewer_qsJd", "summary": "The paper trains a single latent-conditioned policy built on a forward-backward (FB-CPR) backbone, that claims to handle motion tracking, sparse-reward tasks, and goal-pose reaching with no task-specific fine-tuning. Training is fully off-policy in Isaac Sim with heavy domain randomisation. The same network is then deployed zero-shot on a Unitree G1, with optional latent-space CEM/dual-loop annealing (Dial-MPC) for quick adaptation. Evaluations span simulations and real hardware, highlighting robustness in balance, recovery, and payload handling.", "questions": "## Questions / Requested Revisions\n\n- Please add comparisons to competitive baselines (e.g., re-run Ex-Body 2, OmniH2O, Puppeteer on the same tasks and report identical metrics like MPJPE for tracking).\n- Expand the evaluation to include error tracking, and behaviour diversity metrics. Provide full breakdowns in the appendix.\n- Quantify the latent space semantics with metrics such as cluster-purity scores, linear-probe accuracy for task prediction, or mutual information to substantiate claims.\n-  Release training artifacts, including scripts, Docker images, MPC optimization parameters, and safety limits used on the G1, to facilitate reproducibility.\n\n\nOverall, the work extends BFM to real humanoids with solid engineering, but substantial overlap with prior BFM reduces novelty.\nStronger sim-to-real evidence via detailed real-robot metrics, baselines, and stress tests is needed to convincingly demonstrate progress.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "## Strengths\n\n- Real World Demonstrations: Impressive zero-shot performance on the Unitree G1, including balance maintenance, push recovery from large perturbations, and handling a 4 kg payload, showcases practical sim-to-real transfer.\n- Clear Description of Method: The FB backbone, critics, reward shaping, and auxiliary components are explained adequately, though familiarity with prior FB-CPR work is helpful for full understanding.\n- Thorough Ablations: Quantitative evaluations of privileged vs. proprioceptive sensing, domain randomization effects, and sim-to-sim robustness (e.g., Isaac to MuJoCo) provide strong evidence for the design choices.", "weaknesses": "## Weaknesses\n\n- Lack of Competitive Baselines: The paper does not compare against state-of-the-art methods like Ex-Body 2, OmniH2O, or Puppeteer on identical tasks and metrics, making it hard to gauge relative advancements.\n\n- Under-Quantified Latent Space Claims: T-sne plot of latents looks interpretable, But assertions about the \"promptable\" and semantic nature of the latent space are not backed by quantitative measures, weakening the foundation model claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2o1cjwaJUW", "reviewer": "Reviewer_NnXb", "summary": "This work presents BFM-Zero, an unsupervised RL method that learns a well-structured latent representation of whole-body motions. The authors hereby demonstrate zero-shot task performances on a physical humanoid, bringing unsupervised RL and forward-backward models to actual hardware. BFM-Zero is trained using a motion capture dataset to regularize the large space of behaviors towards more useful behaviors, reducing the search space immensely. It then allows for solving various tasks such as motion tracking or goal reaching in a zero-shot fashion, as well as allows for few-shot adaptation in the real world, where the environment can be modified, for example, by adding additional weight onto the robot. The paper shows that adding common tricks in sim-to-real transfer, such as domain randomization or privileged information into FB-CPR, can successfully transfer to the real world. \n\n**Main contributions:** \n- Extending the FB-CPR work to real robots, which includes adding Domain Randomization (DR) and an auxiliary rewards\n- Presenting a few-shot adaptation via sampling-based optimization for hard-to-achieve tasks.\n\n**Explanation of rating:** Overall, the paper is a first of its kind and shows zero-shot RL on a humanoid robot for whole-body control. However, from a technical perspective, it is not fundamentally different from previous work, such as MetaMotivo (Tirinzoni et al., 2024), and applies previously known techniques for sim-to-real transfer. While its main contribution lies in the transfer to the physical world, it lacks quality in evaluating results there. Given that this is one of the first attempts to bring unsupervised RL and FB to real humanoids, I still lean towards acceptance, but advise the authors to significantly improve the quality of the exposition and extend the real-world evaluation to provide a more substantial contribution.", "questions": "- In the videos, the robot seems a bit unstable, often drifting in a direction. The authors report MPJPE metrics; however, this does not include the root linear and angular velocities, which seem quite off, judging from the videos. It is not clear to me why the root linear velocity is not part of the observable state. I would assume this could mitigate some of this behavior. Could the authors elaborate on this design choice?\n- The Auxiliary critic is a new component added to the framework. However, it is not ablated in any of the results. How important is this part for the sim-to-real transfer at the end? In general, how important is DR? While reading, I was wondering how FB-CPR would perform on a robot, and which components in this work represent the main changes?\n- The distribution of the rewards seems very interesting (Fig. 3), especially the observation that there seem to be severe collapses. The authors hypothesize that domain randomization has led to this. move-ego-0-0 appears to be a static pose (judging from the supplementary video where the motion is shown). If the hypothesis of the authors is true, why do we only see this in some of the motions? Investigating for which behaviors this occurs would be interesting.\n- The evaluation of the latent space is minimal. e.g., the interpolation result is not surprising; most likely, all those states in between for raising the hand are part of the dataset as well. What happens if we interpolate between dancing and crouching motions? How does the robot's behavior change in those more challenging scenarios where we interpolate trajectories in space and time? \n- The results on the Booster T1 robot are incomplete. It would be interesting to understand if there are weaknesses of the method when applied to this robot, beyond the obvious mechanical limitations. For example, do the robot's specific capabilities affect the training of BFM-Zero in any way (e.g., do the discriminators remain similarly stable)?\n- It would also be helpful to see the selected AMASS goal poses for the goal-based evaluation (Fig. 13).\n- It is not clear to me why the authors selected 175 motions from the CMU subset of AMASS. This subset contains many walking cycles, which, according to the latent-space plots, appear to be well covered. In simulation, it would be straightforward to test on the entire AMASS dataset, thereby improving understanding of out-of-distribution behavior. \n- Why did the authors not train on the much larger AMASS data? How do the discriminators scale with more data, or do we observe mode-collapse?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The authors show promising results for Zero-shot RL on humanoids. The video presents target pose tracking and continuous dancing, as well as few-shot adaptation in the real world on a challenging hopping example.\n- The results are interesting. Especially the natural recovery, the few-shot adaptation, and motions with whole-body contact are impressive.", "weaknesses": "In general, the paper seems rushed and contains many sections with obvious errors (see below for minor issues caught during the read). This is also true for the evaluation, which raises more questions than it answers (see the question section). \n\n- **Major:** The main contribution of this work is the presentation of a method that works on a real humanoid. However, beyond the few-shot adaptation task, there is little quantitative evaluation in the real world. Furthermore, the results presented on the robot do not demonstrate the method's limits. While it is clear that this formulation comes with many new advantages, I would be interested in understanding where the limits and gaps are from a motion tracking perspective. We have seen quite expressive motion-tracking capabilities in, e.g., He et al., ASAP; how close do we get to such performances? How much expressivity is encoded? Different experiments could help understand the performance in the real world as well as help understand/interpret the gap between the results presented in Fig. 3 and real-world results, e.g., how much can we trust those numbers for the real world? For example, to better understand the sim-to-real gap, it would be helpful to pick 1-2 examples and compute the MPJPE in the real world. This would give a sense of how much the metrics might vary between simulation and reality.\n- A Related Work: In general, the related work is incomplete, rushed, and oversimplifies the progress in the field of robotics. There has been significant work before 2025 that has investigated and enabled versatile human motion on robots.\n    - DR is a well-explored technique with methods dating back to 2017, such as (Peng et al., Sim-to-Real Transfer of Robotic Control with Dynamics Randomization)\n    - Bringing bigger datasets of whole-body human motion beyond locomotion to robots has been investigated before 2025 in many works, some also identified by the authors (He et al., 2024, HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots), but also some not mentioned in this section, such as (Cheng et al., 2024, Expressive Whole-Body Control for Humanoid Robots), (He et al., 2024, OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning) or not included at all, such as (Serifi et al., 2024, VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters), (Fu et al., 2024, HumanPlus Humanoid Shadowing and Imitation from Humans), (Dugar et al., 2024, Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots), (He et al., 2024, Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation), most of this work is from robotics conferences.\n\n- Minor:\n    - L139, L996: broken reference.\n    - Figure 3: missing values.\n    - Some tables are figures.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "g4TXbmIuv4", "reviewer": "Reviewer_qyhf", "summary": "The authors propose an approach for Unsupervised RL pretraining of Behavior Foundational Models (BFMs) using a recently proposed technique called Forward-Backwards (FB) representation. The approach presented by the authors can essentially be framed as putting together existing techniques and scaling FB method to humanoid whole-body control, so that it could be prompted for different tasks without retraining. Moreover, they even propose a technique for fast adaptation, finetuning and high-level planning of their model.", "questions": "What are design choices in this approach which make it work significantly better than previous approaches?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The authors successfully put together a method from existing related approaches, such that it scales well to complex humanoid whole-body control. Their approach can successfully be used in zero-shot manner, as well as being used for fast adaptation and finetuning. This is a good empirical contribution since existing methods do not enjoy such properties.", "weaknesses": "I think authors could do a better job for a reader to make them understand how this work differs from existing approaches such as [1] and what are the most important differences which make their method work. Currently, the proposed approach differs in a few components:\n* Different RL algorithm\n* Section 2.2 also proposes a few techniques such as Domain Randomization (DR) and Reward Regularization (RR)\n\nI think disentangling these design choices is important for a reader to understand why this approach works better. I think having additional ablations on DR and RR will greatly improve paper's technical presentation.\n\n\n[1] Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models, Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta, 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)