WholeBodyVLA: Towards Unified Latent VLA for Whole-body Loco-manipulation Control
Authors: Haoran Jiang
Jin Chen
Qingwen Bu
Li Chen
Modi Shi
Yanjie Zhang
Delong Li
Chuanzhe Suo
wang chuang
zhihui peng
Hongyang Li
Venue: ICLR 2026 Poster (2026)
Keywords: Humanoid Robots
Vision-Language-Action Model
Locomotion and Manipulation
Whole-Body Control
Reinforcement Learning
Primary area: applications to robotics, autonomy, planning
TL;DR: We present WholeBodyVLA, a unified VLA framework enabling large-space humanoid loco-manipulation via unified latent learning and loco–manipulation–oriented RL.

Abstract: Humanoid robots require precise locomotion and dexterous manipulation to perform challenging locomanipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion. This confines the robot to a limited workspace, preventing it from performing large-space loco-manipulation. We attribute this to: (1) the challenge of acquiring loco-manipulation knowledge due to the scarcity of humanoid teleoperation data, and (2) the difficulty of faithfully and reliably executing locomotion commands, stemming from the limited precision and stability of existing RL controllers. To acquire richer loco-manipulation knowledge, we propose a unified latent learning framework that enables Vision-Language-Action (VLA) system to learn from low-cost action-free egocentric videos. Moreover, an efficient data collection pipeline is devised to augment the dataset and scale the benefits. To more precisely execute the desired locomotion commands, we present a loco–manipulation–oriented (LMO) RL policy specifically tailored for accurate and stable core loco-manipulation movements, such as advancing, turning, and squatting. Building on these components, we introduce WholeBodyVLA, a unified framework for humanoid loco-manipulation. To the best of our knowledge, WholeBodyVLA is one of its kind enabling large-space humanoid loco–manipulation. It is verified via comprehensive experiments on the AgiBot X2 humanoid, outperforming prior baseline by 21.3%. It also demonstrates strong generalization and high extensibility across a broad range of tasks. Code and checkpoints would be made public.

Published as a conference paper at ICLR 2026
WHOLEBODYVLA: TOWARDS UNIFIED LATENT VLA FOR WHOLE-BOD LOCO-MANIPULATION CONTROL
Haoran Jiang 1,2,4,3+ Jin Chen 1,2,4+ Qingwen Bu² Li Chen²
Modi Shi
Yanjie Zhang
Delong Li Chuanzhe Suo Chuang Wang² Zhihui Peng Hongyang Li
Fudan University
OpenDrrieLLab & MMLab at The University of Hong Kong 3 AGIBOT "SII
"Equal contribution Project co-lead
1m5m
(6)
(b)
Gc)
(d)
Urieenn
(h)
Figure 1: Introducing WholeBodyVLLA, a humanoid system that operates on Agibot X2 robot and performs end-to-end humanoid mocommanipulation large space for the first time. The proposed system achieves consecutive tasks autonomously, including (a-c) bimanual grasping, side-step to- wards the box, and squatting to place; (d-e) squatting to grasp and lift the box, and turning to place the box onto the cart; (f-h) grasping the cart handle, pushing the cart forward, and pushing a load of more than 50 kg. See https:/ opendr i velab. com/ WholeBodyVLA.
ABSTRACT
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modu- locomotion. This con- lar or end-to-end, are deficient in manipulation-awan fines the robot limited workspace, preventing it from performing large- space loco-manipulation We attribute this to: (1) the challenge of acquir- ing bcco-manipulation knowledge due to the scarcity of humanoid teleopera- tion data, and (2) the difficulty of faithfully and reliably executing locomo- tion commands, stemming from the limited precision and stability of existing RI. controllers. To acquire richer loco-manipulation knowledge, we propose a unified latent learning framework that enables Laniion-annnaa-Atttion (VLA) system to learn from low-cost action-free egocentric videos. Moreover, an ef- ficient human data collection pipeline is devised to augment the dataset and scale the benefits. To execute the desired locomotion commands more precisely. we present a loco-manipulation-oiinnne
(LMO) RL policy specifically tailored for accurate and stable core loco-manipulation movements, such as advancing, turning, and squatting. Building on these components, we introduce Whole- VLdy unified framework for humanoid doco-manipulation To the best of our knowledge, WholeBodyVLL. VLA is one of its kind enabling large-space hu- manoid loco macemmanpplation. It is verified via comprehensive experiments AgiBot X2 humanoid, outperforming prior baseline by 21.3%. It also demon- strates strong generalization and high extensibility across a broad range of tasks.
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INTRODUCTION
Humanoid robots are widely regarded as the leading embodiment for realizing general-purposs em- bodied agents-systems -systems that can perceive, reason, and act the open-ended, human-centered en- vironment. Realizing this vision requires close coordination between dexterous manipulation and agile locomotion. Despite notable progress in RL-based whole-body imitation (Ji et al., 2024; He et al., 2025b;a; Truong et al., 2025) and loci-maninllation controllers (Ben et al., 2025; Zhang et al., 2025a; Li et al., 2025b; Shi et al., 2025), and the recent surge of Disiio-Language-ei Action (VLA) sys- tems for in-place manipulation (Brohan et al., 2023; Kim et al., 2024; Liu et al., 2025; Intelligence et al, 2025; Yu et al., 2026; Yang et al., 2026), autonomous policies for humanoid mani-maninnlation remain limited. A key challenge is manipulation- ments that actively create the preconditions for the intended aanipulation-approcchhe and stabilizing- rather than treating locomotion and manipulation as separate stage.
locomotion: planning and executing move-
orienting,
A naive solution is to serialize locomotion and manipulation with high-level planner, selecting and switching between them (e.g. navigation vs. grasping) (Yuan et al., 2025; Zhang al., 2025b; Chen et al., 2025b). However, the limited closed-loop feedback and lack of end-to-end joint optimization could lead error accumulation, resulting in robot configurations that are suboptimal for subsequent manipulation tasks. Another promising option is via the end-to-end framework (Ding et al., 2025; Bjorek et al., 2025; Boston Dynamics, 2025). It executes whole-body control directly and may alleviate the handoff issues of modular pipelines. However, training such end-to-end policies via imitation learning requires large-scale whole-body data, which are difficult to obtain.
To this end, we contend that the most fundamental concern to overcome is data scarcity, which prevents the acquisition of aoco-manipulation knowledge. Large-scale datasets have proven critical in tabletop manipulation (Padalkar et al., 2024; Bu et al., 2025a) and in navigation quadruped platforms (Anderson et al., 2018; Ku et al., 2020; Zhang et al., 2026). However, these separate tasks. In contrast, datasets that integrate
wheeled or
resources treat manipulation and navigation humanoid locomotion with manipulation are almost few. Collecting such trajectories at scale, either via Motion Capture (MoCap) or teleoperation, is prohibitively expensive. Without such data, models lack the requisite experience to learn locomotion behaviors that fulfill the manipulation scenarios sufficiently in real-world deployments.
In this work, we explore how to learn loco- aoto-mmppplaatinn behaviors from low-cost action- frreen videos, alleviating the scarcity of teleoperation data. Humans naturally acquire new woco-manipulation skills by watching others, and prior work in tabletop manipulation has shown that human demon- stration videos can be highly effective (Ye et al., 2025; Bu et al., 2025b; Bjorek et al., 2025). We argue that the same intuition applies to humanoid iec-manipulattinn videos already expose key information such as locomotion direction, end-effector trajectories, object affordances, and physical interaction cues. By contrast, teleoperation data offers robot-aligned actions ready for direct imita- tion learning but require costly hardware and skilled operators, hindering large-scale collection.
Motivated by this gap, we focus the pre-training stage of VLA and introduce unified latent learning, which acquires large-scale loco mooo-mmaiulaiionn priors from human egocentric videos and uses them latent supervision for the VLA. Unified latent learning operates by turning action-free videos into discrete latent actions. Since videos lack robot-aligned labels and therefore cannot be used for direct imitation learning, we train latent action model (LAM) encode frame-to-frame inverse dynamics into a compact discrete latent space. Because locomotion and manipulation exhibit fundamentally different visual change patterns, we train a locomotion LAM and manipulation LAM separately. For the locomotion LAM, we use our self-collected egocentric manipulation- aware locomotion videos and design a simple capture pipeline requiring only a single operator with a monocular camera. For the manipulation LAM, we rely on AgiBot World, one of the largest real- robot manipulation datasets. After LAM pre-training, the VIA is trained on mixed human-video and robot-data under joint supervision from both LAMs, ensuring coherent intention prediction across locomotion and manipulation. After pre-training, we attach a lightweight action decoder and fine-tune the VLA on teleoperation trajectories, grounding the latent actions into rebot-executable commands: boder-body joint positions and a locomotion command for the lower body. A high- frequency RL controller then converts the locomotion command into reliable lower-body actions.
While unified latent learning provides rich supervision for training the high-level VLA, the robot may still fail locomotion execution due to limitations of the low-level RL controller. As shown in
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Table 1: Comparison of autonomous humanoid control systems. We achieve various loco- manipulation tasks in the real world through full-body control, without external modules. Manipu- lation reflects single vs. coordinated dual-arm tasks, while Locomotion covers stepping (lateral and forward/backward), turning, and squatting. Information summarized from demos.
Method
VL Inpot
Manipulation
Locomotion
Closed-LLoop
Multi-Task
No Extra Info.
HOME [Ben et al., 2025)
vision
single arm single arm
X
AMO IL. (Li al, 3025c)
vision
&
X
FALCON Planeer (Zhang et al,, 2025a)
visson
dual arm
C
b
/
X
MoCap impal, object pose
R²S* [Zhang all, 2025h)
x vision, text
arm
/
X
MoCapp input
LeVERB (Xue et al. 2025)
&
HITTER (Su al., 2025)
single arm
MoCap input
Homancid-VLA (Ding 2025)
vision, text vision, text
x
GROOT (Bjorck et al., 2025)
dual arm
x
/
Being-0 (Yuan et al., 2025)
vision, text
dual arm
x
GPT detector
1BBM (Boston Dynamies, 2025)
vision, text
arm
MoCap data
HEAD (Chen et 2025b)
visson
navigation goal
WholeBody VLA (Ours)
vision,
C & / /
the failure-case statistics in Appendix C.3, many errors-such as stumble, path deviation, and turn with aaiva from the limited precision and stability of the underlying RL controller rather than from the VLA itself. A key contributor is the continuous velocity-tracking objective used in existing locomotion RL controllers. While suitable for broad locomotion behaviors, this objective exceeds the actual needs of moc--paanpulation, making the controller harder to train and less reliable for fine-grained positional control. To address this, we introduce a Doco-manipulation cooilltioooorieneeddd (LMO) RL policy that employs simplified discrete command interface. This design is specifically tailored for accurate and stable execution of fundamental loco-manipulation movements such as advancing, turning, and squatting, enabling more precise and dependable low-level control.
Based on these designs, we introduce WholeBody VLA, a framework for efficient training and de- ployment of autonomous control policies on a bipedal humanoid. enables real-world, large-space end-to-end humanoid loco- moro-mmnilllaiion.. As summarized in Table I, carlier systems either decouple key components or support only partial loco- mecemmaapplaaiion whereas WholeBodyVi VLA consolidates them within single unified framework. Comprehensive experiments WholeBodyVL VLA surpasses prior baselines by 21.3% and 24.0%, while demonstrating strong gener- alization and broad task coverage. Our contributions could be summarized follows:
the Agibot X2 show that
We present WholeBody VLA, VLA framework that enables the bipedal humanoid to perform end-to-end large-space loco-manipulation in the real-world setting autonomously.
We introduce unified latent learning, enabling joint coomotionnmanipulation learning from abundant low-cost action-free videos and alleviating the scarcity of teleoperation data.
We propose a leco-maipulation--riinnce (LMO) RL policy that mitigates decision-executionn misalignment via a discrete command interface tailored for recommanpuulaton.
RELATED WORKS
2.1 HUMANOID THOLE-BODY CONTROL
Loco-manipulation controllers. To move beyond isolated manipulation or motion imita- tion (Cheng al., 2024; Ji et al., 2024; He al., 2024; 2025b;a; Ze al., 2025; Truong al., 2025; Chen et al., 2025c; Xie et al., 2025; Wang et al., 2025; Cheng et al., 2025; Fu et al., 2025; Pan et al., 2026), number of RI-based whole-body controllers have been proposed, most of which adopt a velocity-tracking interface, optimizing per-step errors across commanded speed (Ben et al., 2025; Shi et al., 2025; Li et al., 2025a; Zhang et al., 2025a;b; Sun et al., 2025). While sufficient for cruis- ing, this formulation leaves start-stop semantics implicit, induces fragmented gaits across speed, and provides little supervision for episode-level controllability such as braking precision or head- critical for coco-mmnppllttoo.. Upper-bode influence typically modeled
ing Mieelity-capabili as task-agnosti noise or motion clips, which sparsely reflect the structured inertial couplings of real tasks (grasp, lift, push), limiting stability under load. Methods like HOMIE's PD-stabilized
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arms (Ben et al., 2025), AMO's trajectory-optimizatin hybrids (Li et al., 2025a), FALCON's force curriculum (Zhang et al., 2025a), R² skill libraries (Zhang et al., 2025b), or ULC's unified resid- ual controller (Sun al., 2025) improve robustness but inherit the limitations of velocity-centric training, yielding inconsistent gaits and unstable teleop-trajectorie that hinder low-level stability and high-level VLA policy learning.
High-level planners for humanoids. Moreover, RL controllers generally lack the ability to process RGB vision or language inputs directly, they are not sufficient for autonomous task execution. To address this, complementary line of work has explored high-level planning for humanoids. LE- VERB (Xue et al., 2025) embeds latent verbs into RI for low-level WBC control. Other systems like R (Zhang et al., 2025b), Being-O (Yuan et al., 2025) and HEAD (Chen et al., 2025b) employ modular planners driven by vision-language models (VLMs) that sequence locomotion and manipu- lation as discrete skills. While conceptually appealing, these frameworks are hindered by brittle skill Noundaries-robble
often end up in unstable or muskinnfasiblle configurations after locomotion and by reliance on cloud-based perception, which introduces latency and undermines real-time con- Laat----nnngge-cttiiin frameworks to (Ding et al., 2025) focuses on locomotion, while
trol. In parallel, initial efforts have attempted to extend humanoid robots. For instance, Humanoid-Vi GROOT (Bjorck al., 2025) targets manipulation for humanoid embodiments; each emphasizes one modality while neglecting the other primitive critical for seamless loco- loco-manipulation task execu- tion. The Boston Dynamics demonstration (Boston Dynamics, 2025) is constrained to limited workspace, yet relies heavily on expensive MoCap collections of whole-body loco- ceco-manipulation These limitations highlight the need for unified frameworks that couple vision and language with whole-body control, enabling humanoid loco-manipulation without brittle modular boundaries.
2.2
VISION-LANGUAGE-ACTION MODELS
Recently, building on multimodal foundation models and imitation learning from large-scale real- robot trajectories, VLA systems have garnered broad attention for their strong generalization and dexterous manipulation capabilities Chen et al. (2025a). Representative efforts include RT-2 (Bro- han et al., 2023), Open VLA (Kim et al., 2024), RDT (Liu et al., 2025), PiO (Black et al., 2024), and Pi0.5 (Intelligence et al., 2025). Nevertheless, these models typically emphasize upper-body ma- nipulation only and do not provide unified, end-to-end solution for the autonomous whole-body body control required by loco moco-mmappulationn tasks. In contrast, our aim is a unified VLA that integrates locomotion and manipulation, enabling bipedal humanoids to perform laci-manipulation tasks.
Latent action learning. Despite rapid progress, current robot datasets remain far smaller than those in the vision and language domains. The core bottleneck is the cost of action-labeled trajec- tories exiees-v teleoperation systems, skilled operators, and substantial collection time. Latent action learning sidesteps this: instead of explicit action labels, it compresses frame-to-frame visual changes into compact, discrete tokens that supervise policy learning from action-free videos. Rep- resentative approaches include Genie (Bruce al., 2024), LAPA (Ye al., 2025), IGOR (Chen et al., 2024) and UniVLA (Bu et al., 2025b). Collectively, these studies demonstrate that abundant, cross-embodiment, action-free videos can be transformed into effective supervisory signals for VLA training. Motivated by this-and noting that large-scale humanoid loco- monomuuaillaio data are even harder to obtain our work performs latent action learning for locomotion and manipulation a unified manner, enabling humanoids to perform laco-manipulation with strong generalization.
3 WHOLEBODYVLA
WholeBodyVLA, which equips VLA models with locomotion primitives that reliably es- tablish the preconditions for manipulation, leverages unified latent learning and peco-manipulation-orriettdd (LMO) RL policy to enable humanoids to accomplish long- range, large-area tasks. In this section, we detail (i) how unified latent learning contributes to WholeBody jointly master manipulation and locomotion primitives, and (ii) how our cooo-maappllaiion-oreenedd RL policy explicitly optimizes stability under dynamic disturbances.
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Unified latent learning
manigulation
1000000
41
1000000
a aupper
LAM
latents
Action Decoder
Lacomotion
LMO RL Policy
locomotion
Lower
VLM
Precise locomotion
Stable manipulation
Image Observation
Task Instruction
Pick the box, tuen to the cart and place the box on the sent.
Upper: Band. Kanip.
Cmd:
4
Aoco-manipulation- oriented RL
Reward:
Pipeline of WholeBodyVLA. LAM is pretrained manipulation and manipulation- aware locomotion videos, yielding unified latent supervision for VLM. Meanwhile, the LMO RL policy is trained for precise and stable locomotion under disturbances. At runtime, egocentric images and language instructions are encoded by the VLM into latent action tokens, which are decoded (~ 10 Hz) into (i) dual-arm joint actions and (ii) locomotion commands executed by LMO at 50 Hz, enabling robust whole-body aco-manipulation.
Figure 2:
3.1 UNIFIED LATENT ACTION MODEL.
Our core idea is to learn manipulation and locomotion primitives from egocentric manipulation and manipulation-awar locomotion videos with Latent Action Models (LAMs), and then supervise VLA training. However, find that directly training single LAM the mixed data yields sub- optimal performance. We attribute this to the fundamentally different modalities of the two data sources: manipulation videos the camera pose is almost static, whereas in locomotion videos changes continuously. This discrepancy degrades LAM training in two ways. First, image varia- tions manipulation data dominated by arm motion, biasing the model attend arm regions; conversely. image variations in locomotion data arise mainly from environment motion relative to the moving camera, forcing the model to focus on the entire scene. The conflicting attention objec- tives hinder stable representation learning. Second, in manipulation data the LAM learns to encode changes in arm-environment relative position as arm motion, whereas in locomotion data (where the arm often remains in the FOV, especially in loco-manipulation tasks) the same relative posi- tion changes are caused by camera motion. The LAM may mistakenly interpret these changes as arm motion instead of locomotion, leading to ambiguous latent encodings. Therefore, train two LAMs separately: a manipulation LAM on the manipulation data and locomotion LAM on the locomotion data. Both LAMs then used jointly supervise the VLA training.
Specifically, following Genie (Bruce et al., 2024) and UniVLA (Bu et al., 2025b), we adopt a VQ- VAE architecture (Van Den Oord al., 2017) and build the encoder top of DINOv2 (Oquab al., 2024) features. Given consecutive frames (0r, oc+k), the LAM encoder & first emits continuous latent vector 2, (or, 0t+k), which then quantized to the nearest entry in the learned codebook: cf arg min.ec || c|22, E C, The index i E {mani, loco) denotes the manipulation LAM and the locomotion LAM, respectively. To train both the encoder E, and the codebook C., the LAM decoder D; receives the former frame and the quantized latent action, and is trained to reconstruct the latter frame Ô++ (,(o,, Reconstruction optimized by minimizing the standard VQ- VAE loss. With the LAMs pretrained, we then train the VLA policy No to jointly predict both types of latent actions given visual observations and task language with Cross-Entropy loss, maximizing
Te(cmanni Joco Or, e),
(1)
where & represents language instructions. This unified prediction compels the model to learn how locomotion and manipulation interact in single, cohesive action space support task execution.
Finally, to execute on humanoids, we introduce a lightweight decoder that grounds latent actions into robot-specific commands: a, [((mani 2/oco, st), where 8t is the robot state. The decoder
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produces (i) upper-body joint angles and (ii) locomotion command indicating the action to be executed. This command is translated by our LMO RL policy to lower-body torques. Through this division of labor, the VLA provides unified latent decisions, the decoder grounds them into emboodiment-specffic control signals, and the RI policy ensures stable execution-reaaizzing whole- body loco- maco-manipulttiinn in practice.
Manipulation-aware locomotion data collection. To further scale the gains of unified latent learn- ing, we propose to exploit large amounts of human egocentric manipulation-wwarc locomotion videos, which can be easily collected. We design an egocentric data collection pipeline character- ized by: (1) low cost and efficiency. only single operator with a hhad-mounted camera is required, avoiding expensive MoCap or teleoperation; (2) coverage of humanoid primitives, operators should perform all types of motion, such as advancing. turning, and squatting; and (3) goal-directed exe- cution, operators should perform locomotion to contact potential manipulation goals, ensuring that the locomotion data directly aligned with loco coro-mmaniplatioon learning. More details, including the framework, training objectives, and data collection pipeline, are provided in Appendix A.I.
3.2
LOCO-MANIPULATION -ORENNTE RL POLICY
As discussed in Section 1, a major failure mode in loco- manipulaannno is the misalignment between high-level decisions and low-level execution. This issue largely arises from the continuous random bllacit-tracking objectives used in existing RL controllers-oojectivess designed for broad loco- motion rather than the stable, reliable start-stop and directional control needed for manipulation. In this section, we introduce Loco-ManipulationOrieeee velocity tracking with a discrete command interface, enabling more faithful execution.
(LMO) RL framework that replaces
Observation space. The policy relies solely on proprioceptive egocentric states with short history stack: O = [ee Wi, gt, qt qt, a,-1], including base angular velocity, gravity vector, joint states, and the previous action. This compact design avoids reliance privileged environment information while remaining sufficient for closed-loop stability.
Discrete command interface. We formulate lower-body control as goul-conditioned regulation, where the policy executes discrete high-level commands while maintaining balance. At each time step, the planner generates a command u, = [Sg,Sy: Sy: Sw, h-] {-1, 0, 133 x R. where Sas Sys Se denotes discrete indicators for forward, lateral, and turning, and h* specifies the stance height. Un- like velocity- -baed formulations, our interface enforces explicit start-stop semantics and reduces trajectory variance, improving the training process of both RL controller and high-level planner.
Reference shaping. Since the inputs are ternary flags, we specify goal speed magnitude of > 0 for each axis, with the sign determined by the intent Sy. To avoid abrupt accelcrations, directional intents are passed through smooth gating function:
Us (t) goal tanh [a(s (t)].
=
(x + (1 A)au(t - 1) + Ask
(2)
for k E {x.y. y. }.. where S is the exponentially smoothed flag. This design ensures predictable on/off transitions and reduces oscillations.
Two-stage curriculum. We adopt a two-stage training scheme that first acquires minimal loco- motion skill and then specializes it for precise and stable loco- aeoo-mmaipulation.
Stage I (basic gair acquisition). For each axis k E {a, y. }}. if * 0 we sample goal speed magnitude ofoaa [0, =) with the sign determined by sgi otherwise of 0. For the upper body, following HOMIE (Ben et al., 2025), the arms track pose targets resampled at a fixed interval and interpolated for smooth motion, while the joint limits gradually relaxed by curriculum factor, exposing the legs to progressively stronger disturbances. This stage enables the policy to develop a basic gait that prevents falling, providing stable foundation for later refinement.
Stage Il (precision and stability). Stage II further targets leco-manipulation-leeell precision and stability through dedicated optimization. On the locomotion side, we fix per-axis cruising speed to constants gogl τ to standardize cruising and suppress unintended heading drift when no yaw intent is given (s = 0 at onset/offset should not induce yaw). Directional accuracy is measured by the terminal deviation:
Jair wwap v/start)
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where episode begins when any axis flag flips 0++11 and ends when it returns to 0 and the base stabilizes. Minimizing E[Jai] enforces precise initiation, steady cruising, and consistent braking. On the manipulation side, inject realistic perturbations by sampling short arm-motion segments from Agibot-Wool World (Bu et al., 2025a), interpolating them into continuous signals, and replaying them varied rates with light noise. This forces the legs to compensate for structured inertial couplings rather than unstructured disturbances. Moreover, for stationary episodes (3g sy 0), we add stand-still penalty to discourage unnecessary leg actions:
Istand |a 1
Additional implementation details provided in Appendix A.2. Together, these designs yield sta- ble, repeatable gaits and reliable whole-body coordination, avoiding the fragmented motion patterns often induced by velocity-tracking objectives.
4 EXPERIMENTS
In this section, we aim to answer four key questions: QI. (Section 4.2) Does WholeBodyVLLA enable long-range. large-area cco-maaipulation beyond existing SOTA approaches? Q2. (Sec- tion 4.3) Does learning from action-free videos actually improve performance and reduce reliance on teleoperation data? Q3. (Section 4.4) How does LMO contribute to mro-mmnnpullttio?? Q4.
(Section 4.5) Does WholeBody VLA generalize to long-horizon and extended scenarios?
4.1 SETUP
Hardware, tasks, and data collection. We evaluate on the prototype of Agibot X2 humanoid (7- DoF arms with Omnipicker grippers. 6-DoF legs, 1-DoF waist, and an egocentric Intel RealSense D435i camera). Three tasks comprehensively test loco manipulation: (i) bag packing guckiinggrasspin to kg cart handle
paper bag, sidestepping to a carton, squatting, and placing it inside; (ii) box louding- grasp a box, turning, and placing it onto cart; and (iii) carl pushing- grariggnggaangggggnn and pushing it forward stably. These tasks jointly evaluate dual-arm coordination, squat precision, turning accuracy. and stability under heavy loads. Details of the robot platform and teleoperated data collection (VR + joystick, 50 executions per task) are provided in Appendix B.
Baselines. We compare WholeBodyVLA with representative modular pipelines, including navigation-assisee assissed Modular Design baseline, VLA frameworks GROOT N1.5 (Bjorck et al., 2025) and OpenVLA-OFT (Kim al., 2025), both adapted to output dual-arm joint actions and the same discrete locomotion command as WholeBody VLA that is executed by our LMO controller, and ab- lated variants of our design, which either remove/replace the LMO or modify unified latent learning (no LAM, manipulation-oni LAM, details and training setups are provided in Appendix B.2.
single shared LAM on mixed data). Full implementation
Training protocol. We follow the standard VLA recipe of large-scale pretraining followed by real- robot finetuning. For WholeBody VLA, pretraining has two steps: Stage pretrains separate manipu- lation and locomotion LAMs large egocentrie manipulation and manipulation-awee aware locomotion videos, and Stage II trains the VLA to predict both latent actions on the same corpus using the LAM codes as pseudo-action labels. For VLA baselines (GROOT, Open VLA-OFT), released pretrained models. In the finetuning stage (Stage III,, all methods are trained on the same Agibot-X2 telcoperation trajectories for all tasks. The LMO controller and its veloeity- -baed base- line (Ben et al., 2025) are trained separately in simulation and kept fixed during telcoperation data collection and final deployment.
their publicly
4.2 MAIN RESULTS
To demonstrate the effectiveness of WholeBody VLA, we design three task suites- each comprising several subgoals and a diverse set of loco-manipulation primitives-to benchmark performance against multiple baselines (Table 2). These suites span a range of real-world challenges: (1) Bag Packing, which requires stable lateral stepping and precise bimanual manipulation; (2) Box Loading, which involves coordinated turning, squatting, and object placement while maintaining balance; and (3) Cart Pushing, which demands sustained forward locomotion and reliable heading control, Across
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Table 2: Evaluation across three tasks. Each task decomposed into two subgoals. Whole- BodyVLL VLA is shown to outperform both modular and end-to-end baselines, with unified latent learn- ing and the LMO both contributing significantly.
Bag Packing
Box Loading
Cant Pushing
Method
Avg. Score
Grasp Bags
Move & Squat
Squar & Grasp
Rise & Turn
Grab Handle
Push Ahead
Modular Design
22/25
12/25
9/25
9/25
22/25
22/25
64.0%
GROUT w/ LMO
20/5
10/25
6/25
4/25
12/25
11/25
42.0%
OpenVLA--OF OFT w/ LMO
19/25
6/25
12/25
12/25
22/25
14/25
56.7%
WholeBadyVIA. VIA (ours)
23/25
13/25
19/25
17/25
23/25
22/25
78.0%
WholeBodyVLA w/o RL
w/ vel.-based RL
22/25
1/25
16/25
3/25
24/25
15/25
54.0%
w/o
15/25
4/25
6/25
16/25
10/25
39.3%
w/ manip. lam
24/25
7/25
17/25
11/25
20/25
14/25
63.3%
w/ shared lam
18/25
11/25
16/25
16/55
20/25
18/25
66.0%
these tasks, WholeBodyWLA achieves consistently higher success rates than modular and end-to- end baselines, indicating better loco mac-mmanipulation behavior in practice.
4.3 How DOES ACTION-FREE VIDEOS CONTRIBUTE TO LOCO MANO-UNLIILAAI
Does learning from human egocentric videos actually improve performance and reduce reliance on teleoperation data?, we compare WholeBody VLA, which performs full latent pretraining before teleoperation fine-tuning, with WholeBody w/o LAM, which skips latent pretraining. As shown in Table 2, the full model improves success rate by 38.7%, indicating that unified latent learning extracts useful priors from action-free human videos and enhances down- stream policy learning. We also note, in passing, that shared-LAM variant performs slightly worse than our separate- design, suggesting that decoupling the two LAMs is beneficial but not the primary factor. Additionally, manipulation in AgibotWorld (i.c., without locomotion pretraining). The full-pretraining model outperforms this manipulation-onff variant by 14.7%, with the largest gains tasks requiring sub- stantial locomotion before manipulation.
To address Q2:
evaluate variant that performs latent learning only in-place
Data scaling under generalization settings. To further assess how latent learning improves model performance and reduces reliance on teleoperation data, we conduct targeted generalization experi- ments under (i) changed start-posee and (ii) changed objects, layouts, and appearance, while keep- ing the language instruction fixed, see full setups in Appendix C.I. We then study how the amounts of latent pretraining and fine-tuning data affect generalization. In the first group of tasks (Fig. (a)), which primarily evaluate locomotion generalization, we compare models pretrained with 0%, 25%, 50%, and 100% human egocentric videos (all using 100% AgibotWorld data). Their aver- age success rates (vertical axis) are plotted against increasing amounts of teleoperation fine-tuning data (horizontal axis). Models pretrained with more human videos consistently perform better. No- tably, with more than 50% human video pretraining, the model matches variants using less than 25% human videos even when fine-tuned with only 25 teleoperation trajectories, whereas the latter require 200 trajectories to achieve similar performance. In the second group of tasks (Fig. 3 (b)), focused on manipulation generalization, we vary the amount of Agibot data used for latent learning (all using 100% human videos). The trends mirror those in locomotion: stronger latent pre- training yields higher success rates and reduces the amount of fine-tuning data required. Together, these results show that human egocentric videos and unified latent learning significantly improve VLA generalization while reducing reliance on teleoperation data. With a fixed fine-tuning budget, stronger latent pretraining consistently yields higher performance.
4.4 How DOES LMO CONTRIBUTE TO LOCO MANIPUMAAILL
We first compare WholeBody with velocity- RL variant the task suites in Table 2. Overall, the velocity- based controller achieves a 24% lower success rate, with 91.7% of this gap coming from failures in the second subgoal of each task, which contains most of the locomotion. To further test both controllers, we also evaluate them on the extended tasks in Fig. 3(c), in- cluding uneven-terrain traversal, long multi-step sequences, and following extended floor markings
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Panchurra na Datt boating Start bencestatio
6ourskattion
Aedi
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variations in robot start-pose and scene appearance, with data-scaling curves. Bottom: comparison on extended tasks with different baselines. See videos on https::/ https///ppedddeeeleeeeee com/ WWt.hooollBBB........
Figure 3: Real-world generalization of WholeBody VLA. Top:
for visual navigation- dominated by locomotion and long-horizon execution. Across these scenarios, the velocity-based RL variant fails substantially more often than WholeBodyVL.N These failures largely arise from the suboptimal behavior the vanilla velocity- controller, which often produces errors such stumbling, path deviation, turning while advancing, independent of the high-level VLA decisions. A detailed analysis is provided in Appendix C.3. Taken together, these observations indicate that our LMO RL policy effectively mitigates this issue-one that may appear minor but is critical for reliable long-range and multi-step loco-mon cero-mmaipulation.
Table 3: Ablations of the LMO design under locomotion accuracy and manipulation stability. Locomotion accuracy is evaluated for forward/hackwand walking, lateral stepping, and turning, ported as position/orientation error (mean + std). Manipulation stability is quantified by CoM sway during standing and squatting; lower better. See details in Appendix B.4.
Locomotion Accuracy (Pos. Quat. Error)
Munipulation Stubility (CoMS)
Method
Borward&&Bakward
Ligh&Right
Turning
Stunding
Squatting
LMO (ours)
0.21 0.05
0.55=1001
0.06
0.05:10.11
0.19
0.03:10.02
0.03::0.02
LMO w/o Eq.
0.24 0.07
0.61:0.02
0.009
0.05:10.01
0.28:10.02
0.04:10.03
0.03:10.22
LMO stage 2
0.27=0.02 0.09=0.01
0.72+0.03
00.11
0.20:1.01 /0.32-0.03
0.05:0.04
0.07-0.03
LMO w/o stage
0,30=0.03
00.0000.01
0.6621.04
/0. 13:10033
0.46:10.01
/0.34:10.04
0.05:10.03
0.0420.03
Vel.-bused policy
0.24=0.04
0..2200.02
/0.10:0000007220.00
0.26:10.01 /0.20:10.0
0.06:1.04
0.05=0.04
Beyond real-robot results, we perform controlled ablations in MuJoCo as shown in Table 3. Re- moving the directional accuracy reward (w/o Eq. 3) degrades turning precision. Disabling Stage II increases trajectory error and squatting sway, showing the necessity of targeted refinement. Without Stage the policy fails to acquire stable gaits, producing the largest errors overall. These results confirm that the discrete command interface, two-stage curriculum, and structured perturbations are all crucial for precise trajectory tracking and stable whole-body coordination.

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4.5 DOES WHOLEBODYVLL GENERALIZE TO LONG-HORIZON AND EXTENDED LOCO- MANIPULAAIAAO SCENARIOS?
We also evaluate the extensibility of WholeBodyVLA by evaluating it set of more challenging scenarios, detailed in Appendix C.I The bottom row of Fig. shows five extended tasks, including traversing uneven terrain, executing a long-horizon multi-step sequence. following floor markings for visual navigation, and everyday loco-manipulate uum cleaning. As shown in Fig. (c), WholeBodyVLLA VLA remains superior across all these settings, indicating the framework scales beyond the benchmark tasks while preserving robust generalization.
activities such wiping a table and vac-
5 CONCLUSION AND FUTURE WORK
We present WholeBodyVLA. VLA enabling humanoid robots perform large-space loco-man mooommnppulation. WholeBody VL. introduces unified latent learning and a LMO RL policy, ef- fectively alleviating the scarcity of teleoperation data well the decision-executee execution misalignment induced by redundant velocity-tracking objectives in prior RI. training. Comprehensive experiments show superior performance, strong generalization and extensibility compared to prior baselines.
Limitations and future work. While effective, WholeBody VLA still faces challenges in handling long-horizon and dexterous tasks. Future work will focus on incorporating lightweight mapping and memory for extended planning and developing active perception strategies to improve robustness in cluttered or dynamic environments. These directions will further enhance its scalability and generalization, paving the way toward versatile real-world humanoid loco- coco-mmnipulation.
ACKNOWLEDGMENT
This work is supported by the JC STEM Lab of Autonomous Intelligent Systems funded by The Hong Kong Jockey Club Charitics Trust. We also gratefully acknowledge the hardware modification support and generous research sponsorship from AGIBOT. We would like to express our sincere gratitude to Hanfu Gai, Yixuan Pan, Yichao Zhong, Chuanchun Lin, Xiangchao Shi, Kun Wang, Minghao Zhang, Hexing Ai and the rest of the members from OpenDriveLab and Agibot X-Lab for their discussions and support throughout this work. We would also like to thank Xingge Qiao, Ziqi Dai and Zhihao Sun for their assistance with video shooting.
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Appendix
A METHODOLOGY
A.1 DETAILS OF LAM AND VLA ARCHITECTURE
This section provides additional details of our VLA, complementing Section 3.1.
LAM training. The LAM encoder & is implemented with spatio-temporal transformer. Given consecutive frames (Or, 0t+k), it generates continuous latent vector:
2 (o,, oc+k):
which is then quantized to the closest entry in the learned codebook:
C quantize(zz) == arg min
2 c/2,
C₁ C..
aEC
Here, i E {mani, loco} denotes whether the LAM corresponds to manipulation or locomotion.
To optimize both the encoder & and the codebook Ci, the decoder Di reconstructs the future frame ôt + k from the current frame o and the quantized latent action cy:
d (o ct).
Reconstruction is supervised with mean mean-squared error loss:
m |0 - 6
In addition, we employ the standard VQ-VAE objective to jointly train the encoder, decoder, and codebook:
LLAM Lyse + [|g|e.]] =ell2 + 8 ||c. sg[=+]12.
where sg[-] indicates the stop-graud -ragdrdddent operator and 3 is the commitment cost.
VLA training. After pretraining the LAMs, train the vision-languann lanno-la-ngggcttion (VLA) policy Ty to predict both manipulation and locomotion latent actions from visual observations and task language. This formulated as maximum likelihood estimation (MLE):
min log (c
(cmani
0z, E)].
This joint prediction forces the policy to model the interaction of locomotion and manipulation in unified latent space for task execution.
Execution on humanoids. For deployment, we use a lightweight exccution decoder f to map latent actions into robot-specific control commands:
a f( clnco st),
,c,
where &; is the robot state, and amani aloco are the latent actions predicted by the VLA policy. The decoder produces outputs: (1) upper-body joint angles for manipulation, and (2) locomotion command for the low-level RL controller.
Manipulation-aware locomotion data collection details. We design a simple yet effective data collection pipeline that requires only single operator wearing a camera to capture data. During collection, we employ two types of cameras: (1) an Intel RealSense D435i RGB-D camera, and (2) a GoPro camera, which provides larger field of view (FOV) and thus enables the collection of more suitable locomotion data for model learning. The operator mounts the camera on the head and performs variety of loco-manipulation tasks. For the manipulation component, the operator not required to actually execute object interactions: instead, suffices to identify potentially manipulable objects and approach them until contact. For locomotion, instruct the operator to perform diverse actions such as advancing, turning, and squatting. A schematic overview of our data collection pipeline is shown in Figure 4.
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Low-cost human data collaction
Maniputation
Latent Action Model
ede X3 f+r
Og
Encoder
Decoder
Locomation
Oc+k
Ot+k
Codebook unitying meniillation
locomotion
Figure 4: Low-cost egocentric data collection locomotion task and pretraining pipeline for LAM. A single operator records egocentric video while executing eight canonical motion primitives toward potential manipulation goals. This task-oriented pipeline captures locomotion patterns that are diverse, structured, and directly relevant to humanoid loco-manij crcommnnpulltion. Then the collected manipulation-awes -ware locomotion data along with the open-source in-place manipulation dataset are used to pretrain the latent action model in VQ- VQ-Vee-t pipeline.
A.2 DETAILS THE
RL POLICY
This section provides additional details of our Aoco-Maanpplationn Oriiented (LMO) RL policy, complementing Section 3.2. Unlike conventional velocity-tracking controllers, which often pro- duce inconsistent gaits when tracking different reference speed, our policy operates in discrete intent space (start, stop, tum, squat) that aligns more naturally with loco-manipulation tasks. The following subsections describe the control interface, policy inputs, reference shaping, curriculum design, and additional implementation details.
Problem formulation. We cast lower- body control as goal-conditioned regulation, where the ob- jective is to execute discrete commands faithfully while maintaining balance under manipulation. At each step f, the planner issues
U = [aps Sy, 8th h²1 E (-1,0,1)* x R.
where 8. are discrete start/stop flags for forward/backward (82), lateral (sy), and yaw rotation (sw). and h* is the desired stance height. This command interface provides explicit semantics for start-stop execution, in contrast to velocity-basee objectives that yield inconsistent or unstable gaits when trained across varying reference speed.
Observation space.
The policy receives purely proprioceptive inputs with a short history stack:
O, Ue, W., gx, qr. dr. a
where w, E R is the base angular velocity, g: E R the gravity vector, qr, q the joint positions and velocities, and a the previous action. This design avoids reliance on privileged simulator information and is sufficient for closed-loop balance.
Reference shaping. Because intents Sę are ternary, we convert them into smooth velocity refer- ences to prevent impulsive accelerations:
ref (t) gلسو d(((s 3x (1)), 3x(() - (1 5 -t Ask. uk
where uf is the goal speed magnitude, af-) a saturating nonlinearity, and 5y the smoothed flag. This "soft gate" ensures predictable transitions when toggling intents on or off, implicitly bounding accelerations.
Two-stage curriculum. Stage 1 (basic gait acquisition). In the first stage, the policy learns minimal gait that prevents falls and responds to discrete commands. For each axis k. 8 / we sample goal ([0, u[=*]), otherwise gooal = 0. The upper body follows simple pose targets that resampled fixed cadence, while joint ranges gradually expanded by curriculum factor. This exposes the legs to progressively stronger but aas--agnottic disturbances, establishing a stable baseline gait.
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Stage II (precision and stability). In the second stage, the focus shifts to controllability and bustness under manipulation. Cruising speed are fixed (ut- üg to prevent fragmented gaits. Directional accuracy is enforced by penalizing yaw drift at episode termination:
Jair wrap( 1stan))
where episodes are defined between flag flips 0 + =1. Structured perturbations are introduced by replaying short arm-motion clips from Agibot-World (Bu al., 2025a), interpolated and time- warped:
Wi+1 = min(1,, 4; + (7 5.)
wp = 0,
with L~ Unif [0.8, 2.5], 7 Unif [0.8. 1.5]. and 6; ~ Unif[-0.25, 0.25]. Per-step targets are given by g.a0 g (wi) & with & (O, 0.05²). This structured disturbance forces the legs to compensate for realistic inertial coupling, unlike random perturbations. Additionally, for stationary episodes (8g = By = sp = 0) we apply stand-still penalty
Istand |a 11.
discouraging spurious leg motions and ensuring balance.
Reward functions and domain randomization. Table 4 summarizes all reward terms and weights, separated by category (intent execution, posture, locomotion structure, smoothness, and stability). Novel terms such as yaw-drift penalties, structured perturbation compensation, and stand- still regularization are introduced in Stage II to improve doco-manipulation robustness.
Table 5 details the domain randomization parameters. We randomize dynamics (joint torque injec- tion, actuation offsets, link mass), contact properties (friction, restitution, payload), controller gains (Kp, Ka). and sensory delays (DOF/IMU lag). Stage II increases the strength and frequency of perturbations, including push disturbances and manipulation-innuuee payload variations, to further enhance robustness.
B TRAINING AND DEPLOYMENT DETAILS
For LAM and VLA training, locomotion LAM is trained on our collected low-cost egocentric loco- motion videos, while manipulation LAM is trained from real robot bimanual manipulation datasets. For locomotion data, we collect about 300 hours covering various scenes with our data collection pipeline. For manipulation data, we use AgiBot World (Bu et al., 2025a) dataset. For the shared LAM mentioned in Section 4.3, we train the model mixed data. Because the two data sources are imbalanced size, we perform balanced sampling during training from both sources in every batch. For LAM training, For VLA training, we train from Prismatic-7B and fix the training schedule to 20,000 steps, using total batch size of 1024. We then finetune the VLA with LoRA (Hu et al., 2022) task-specific data. Notably, for the experiments in 4.2, we finetune one model on all three tasks, as opposed to task-specific finetuning. The finetuning process is conducted with total batch size of 64 and lasts for 10,000 steps. All of our LAM and VLA training are performed on 8XNVIDIA H100 GPUs, while our RL policy is trained on single NVIDIA H100.
fix the training schedule to 30,000 steps. using total batch size of 256.
At deployment, the VLA runs on an RIX 4090 GPU workstation, while the RL policy is deployed on NanoPi onboard computer. Communication between the VLA and the robot is handled via Ze- roMQ over Ethernet, enabling low-latency command streaming for closed-loop loco mec-mmanpullttion control. The LMO policy runs 50 Hz on proprioceptive inputs, while the VLA backbone operates at I0 for perception and reasoning.
B. DATA COLLECTION AND HARDWARE DETAILS
Task design. We design three task suites that comprehensively evaluate loco-manipulation capa- bilities by combining various locomotion primitives (forward/backwwrdd walking, lateral stepping, in-place turning, squatting) with manipulation actions requiring single-arm or dual-arm coordina- tion. The first task, bag packing, requires the robot to grasp a paper bag on the table with both arms, sidestep toward nearby carton, squat down, and place the bag inside. This task emphasizes
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Table Reward functions and weights used train the LMO policy, Stage I and Stage II currently share the same scales.
Category
Reward Term
Equation (sketch)
Stage
Stage 11
Forward intent execution
exp(- (1i 8x (ggol)2)
1.5
Lateral intent execution
exp(- (0(44(p (Fy by 118 (2
1.0
1.2
Yaw intent execution
exp( -4 (uyon 8x 1
2.0
2.0
Intent Execution
height tracking
exp( -he M,.e)
2.0
2.0
Vertical velocity suppression
-0.5
-0,75
Angular vel. xy penalty
Whrryll
-0.025
-0.05
Roll/pitch stabilization
g=ll 2
By
-1.5
-1.5
Hip joint deviation
10up 0defoule
-0.2
-0.2
Ankle joint deviation
goesfault 2
-0.5
-0.5
Knee deviation (squat)
penalty on knee angle / height
-0.75
-0.75
Posture Joints
DoF acceleration penalty
1
1-11/1//dt
2.5x
2.5
DoF' pos. limit violation
out,
-2.0
-2.0
DoF' velocity penalty
Σ 02
1x10
1x10-4
DoF velocity limit violation
RELU BRUNE)
-0.002
-0.002
Torque limit violation
Σ RELU Ti
-0.1
-0.1
Foet air time
(ftrat contact) (Toir 0.5)
0.05
0.05
Foot clearance
(pharget -p-}² -p))2.0'
-0.25
-0.25
Foot lateral spacing
l vkl dmmi
0.5
0,5
Knee lateral spacing
Vhe Feight kace
donn
1.0
Locomotion Structure
Feet ground parallelism
Σ Var(HH,
-2.0
-2.0
Feet parallelism
Var(D)
-3.0
-3.0
No-fly penalty
((only foot on ground)
0.75
0.75
Foot slip
1oo0 new
-0.25
-0.25
Foot stumble
1(1= 3/
-1.5
-1.5
Action rate penalty
au11
-0.01
-0.01
Smoothness (2nd-order)
a. 2a.-1 + 2212
-0.05
-0.05
Joint power
w.ll2 0.2
-20x 10
2.0x10-5
Torque usage
E TRP/
2.5x 10-6
2.5x
Energy Smoothness
Feet contact force
Σ RELU Fea))
-0.00025
+000025
Contact momentum
Jof Pil
0,00025
0.00025
Action vanish penalty
max(0, a, -mmr)) +
-1.0
Roll action zero penalty
roll actuator
-0.05
-0.1
Stand-still penalty
| 1/ 1 =,
-0.05
-0.1
Stability
Joint tracking crror
18, plorae
-0.1
-0.1
coordinated dual-arm grasping, lateral stepping, and precise squat execution aligned with the place- ment target. The second task, box loading, requires the robot to squat, grasp a box with both hands, stand up, turn to face cart, and place the box. This stresses squat control, dual-arm stability, and turning accuracy to ensure successful placement. The third task, cart pushing, requires the robot to grasp the handle of 50 kg cart with both arms and push it several meters forward without lateral drift, testing the ability to maintain whole-body stability under sustained external load. These tasks evaluate dual-arm coordination, squat stability, turning precision, and robustness to heavy loads.
Hardware and real-robot loco-manipulation data collection. All experiments are conducted on the prototype Agibot X2 humanoid platform (Fig. 5). Each arm has DoF and is equipped with an Omnipicker gripper the end-effector, the waist provides DoF, and each leg has 6 DoF, supporting a wide range whole-body motions. For egocentric perception, we mount an Intel RealSense D435i RGB-D camera the head, which provides synchronized RGB streams for both locomotion and manipulation tasks. We collect training and evaluation data via physical robot telcoperation. A Meta Quest Pro headset provides egocentric VR teleoperation of the upper body, while locomotion
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Table 5: Domain randomization parameters used in training.
Category
Parameter
Range Setting
Joint torque injection
0..05, 0.05)
Actuation offset
0.05, 0.05
Dynamics
Link mass scale
0.8, 1.2]
COM displacement
[-0.1, 0.1]
Body displacement
[-0.1, 0.1]
Friction coefficient
0.1, 3.0
Restitution coefficient
0.0, 1.0]
Contact Properties
Payloud mass (torso)
[-5, 10]
Payload (hands)
[-0.1, 0.3]
PD gain scaling Kp PD gain scaling Ka
00.9, 1.1]
Controller Gains
0.9, 1.1]
Initial joint position scale
0.8, 1.2]
Push disturbances
velocity up 0.5 m/s
Push interval
every 4
External Disturbances
Init upper-body ratio Delay flag
0.0 enabled
Action lag timesteps
[2, 8]
Latency Sensor Noise
DOF state lag timesteps
[0, 8]
IMU lag timesteps
[1, 10)
commands are issued through joystick. Each of the three tasks is executed 50 times to obtain diverse trajectories for training.
B.2 BASELINES AND ABLATIONS
Modular Design. To emulate modular pipeline, we replace the navigation module with a human tele- operator wearing an FPV headset. The operator controls only locomotion via a handheld joystick, without exposure to data-collection process and relying solely on task instructions and scene con- text. Once navigation ends, control handed over to WholeBodyVLL for manipulation, during which the operator is fully disabled; control returns the operator after manipulation completes. This ensures a fair comparison and provides a near-oracle upper bound for modular pipelines.
Teleoperation
Humanoid Robot
input
Inverse kinematics
Locomotion command
GROOT w/ LMO. GROOT NI.5 (Bjorck et al., 2025) is recent VLA enabling whole-body control. For fairness, we adapt its output: instead directly predicting lower-body joints, GROOT predicts loco- motion commands, which are then executed by our LMO controller. This isolates the high-level reasoning capacity of GROOT from the low-level locomotion stability issues.
Figure 5: Description of the hardware, VR and a joystick are used for data collection.
Open w/ LMO. Since architectural and model-size differences may confound the com- parison with GROOT, we further evaluate Open (Kim et al., 2025), which shares the same Prismatic-75 7B initialization as WholeBody VLA. OLeFVLA-OFT is trained to predict upper-body joint actions and locomotion commands, executed by the same LMO controller in our system.
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Ablations of WholeBodyVLA. We include five variants to assess the contribution of each com- ponent:
(a) WholeBody VLA w/o RL: the VLA directly predicts lower-body joints without the LMO policy.
(b) WholeBody VLA w/ Velocity-Bare -Based RL: replacing our LMO with a conventional velocity- tracking RL controller (reproduced and refined from HOMIE (Ben et al., 2025)).
(c) WholeBodyVLA w/o LAM: the VLA is directly finetuned from Prismatic-78 without unified latent learning.
(d) WholeBody VLA w/ Manipulation LAM: trained only with manipulation latent learning, with- locomotion-aware pretraining.
(e) WLO w/ Shared LAM: unified latent learning is performed on mixed data without modality separation.
These baselines together span modular, end-to-end, and ablated configurations, providing a compre- hensive evaluation of WholeBodyVLA 's design choices.
B.3 PROTOCOLS FOR REAL-ROBOT EXPERIMENT
To ensure fair and reproducible comparison, we adopt the following evaluation protocol: (i) Two independent judges-naive naive to our data-collection process- adjudicatee success/failure for each sub- goal and reconcile to a consensus label. Method order is randomized, and judges are blinded to the active policy mitigate subjective variability. (ii) For each task, subgoals evaluated sequentially: the first subgoal fails, the second is automatically counted as a failure. Each task is evaluated over 25 trials, and we report the mean score across all subgoals.
B.4 DETAILS OF SIMULATION SETUP IN SECTION 4.4
We evaluate full-body humanoid control MuJoCo using the X2 model under fixed simulation time-step and control frequency (as specified by simulation dt and control Secimati ion) to ensure comparability across policies. Two complementary experiments are considered: (i) loco- motion tracking accuracy and (ii) in-place manipulation stability.
Locomotion accuracy. We test three canonical primitives: forward/backward walking (Mzzl = 0.3 m/s), lateral stepping (lvyl = 0.3 m/s), and in-place turning ([wsl = rad/s). Each trial con- sists of 5s active phase with a constant command, followed by a 10s settling phase with zero commands. A reference pose is obtained by integrating the commanded velocity only during the active phase. Metrics are computed exclusively in the settling phase relative this reference, cap- turing the controller's s stop-and-settle precision. We report the mean + standard deviation of (i) position error (m) and (ii) yaw orientation error (rad).
Manipulation stability. We evaluate two postures: standing and squatting (achieved by lowering the stance height). During the test, 14-DoF upper-body trajectories from a sligned.joint h5 are replayed, mapped the robot's URDF joint order. To amplify coupling effects, trajectories are scaled to 2.0x speed, 1.5x amplitude, and perturbed with Gaussian noise (a 0.02). In addition, randomized external forces are applied: horizontal pushes (up to 150 and yaw torques (up to 30 Nm), each lasting 0.2s and injected approximately every 2.5 with temporal jitter. Stability is quantified by the Center-of-Mel Sway (CoMS, m), defined as the RMS deviation the horizontal CoM projection:
CoM Sway- = / /+ {' ||e(t) z|22 dt.
'
(5)
where c(t) 2R2 is the horizontal CoM trajectory and č its temporal mean. Lower CoMS indicates improved balance. We report mean + standard deviation across trials.
Control and observations. Legs are actuated through PD torque control using policy outputs mapped to URDF joints. During stability experiments, the arms follow replayed trajectories; other-
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wise, they remain near rest. Observation construction and joint ordering consistent with training to avoid distribution shift.
C MORE EXPERIMENTS
C.I GENERALIZATION EXPERIMENT DETAILS
We conduct 12 generalization experiments to evaluate how model performance varies under dif- ferent start configurations, scene variations, and task settings (Fig. 3). The detailed setups for all experiments are provided below.
The first group concerns start-pos. pose generalization, where only the robot's initial configuration varies while the table, snack bag, basket, and their relative placements remain fixed unless specified.
(1) Distance (X-Axis). The robot starts at different distances from the table and must approach and place snack bag into the basket. During data collection, start-poses were uniformly sampled between 1.0 m and 1.25 m. For evaluation, start positions of 1.0 m, 1.25 m, and 1.5 m were used (10 trials each). On odd-numbered trials the table appearance matched training, and on even-numbered trials the table color was changed.
(2) Distance (Y-Axis). The robot starts at lateral offsets along the Y-axis. During data collection, start- start-poses poses were sampled uniformly between 25 em and 50 em both sides of the table. Evaluation used offsets of 25 cm, 50 cm, and 75 em on both sides (10 trials per condition, 60 total), with the table recolored on even-numbered trials.
(3) Orientation. The robot begins with different initial headings before rotating toward the table and placing the snack bag. During data collection, orientations were uniformly sampled between +300 and #60.. Evaluation tested +30", #45", and +60 (10 trials each), again alternating table appearance across trials.
(4) Height. The snack hag is placed on tables of different heights while the basket remains on the ground. During data collection, three table heights-60 cm, 45 cm, and 25 em-were randomly chosen. Evaluation included these three heights as well as three unseen heights (55 cm, 40 cm, 20 cm), each tested 10 times.
The second group addresses scene generalization, where visual appearance or object arrangement varies while the underlying task remains fixed.
(5) Unseen Object. The robot manipulates a snack bag not observed during training. During data collection, two designated snack bags are selected randomly each demonstration, with distractor objects on the table. Evaluation involved two novel snack bags, tested over 10 trials each, and all object positions matched those used during data collection.
(6) Unseen Table. The robot performs the same manipulation task on a table with unseen appearance. During data collection, the table, snack bag, basket, and distractors were fixed. Evaluation replaced the table with a visually distinct one while keeping all positions unchanged, and 10 trials were conducted.
(7) Unseen Object Position. The snack bag appears positions outside the training distribution. During data collection, the bag was randomized uniformly within a 30 em x 30 cm region on the table. Evaluation tested two positions lying outside this region (10 trials per position, 20 total).
The final group covers additional locomotion and long-horizer tasks that require more complex behaviors.
(8) Terrain Traversal. The robot receives a bag from a person, follows the person across five terrain types- steps (5 em), foam, wooden planks, gravel, and artificial and returns the bag at the end. We collected 50 demonstrations and evaluated 25 trials.
maintaining balance,
(9) Long-Horizon Horizon Manipulation. The robot walks to table, grasps two specific snack bags, walks to a drawer, places the bags inside, and closes the drawer. 200 demonstrations were collected. Evaluation always used start distance and identical object placement the training setting.
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Average Success Rate
$0.0
20.0
60.0
02.0.0..0
48
40.0
36.0
45.0
30.4
40
24.0
0.0
G.D
0.0
0.0
origin
origin
GROOT w/ LMO
OperVLA-OFT
WholeBodyVLA (ours)
Bag Packing (unseon bag)
Box Loading (unseon contents)
Carr Pushing (unseen loadss
Figure 6: Visual generalization under object and load variations. We evaluate Bag Packing, Box Loading, and Cart Pushing under unseen variations of bag appearance, box contents, and cart loads. WholeBodyVLA consistently outperforms GROOT and Open VLA-OFT across all three tasks, indicating robustness these distribution shifts.
(10) Visual Navigation. The robot follows a trajectory indicated by visual arrow markers on the ground. We collected demonstrations from a fixed start-pose and evaluated 25 trials under the same configuration.
(11) Vacuum Cleaning. floor. Both the start-pose and debris region were fixed during data collection (50 demonstrations) and evaluation (25 trials).
The robot uses vacuum cleaner to remove paper debris scattered on
(12) Wiping Stains. The robot wipes coffee stains from tabletop using cloth. We collected 100 demonstrations, with stain positions uniformly randomized within 30 em x em region and the cloth placement fixed. During evaluation, the same region and cloth configuration were used (25 trials).
Success rates for all tasks reported in Fig. 3. The panel titled "Finetuning Data Scaling for Start-Pose Generalization" presents averages over tasks (1). "Finetuning Data Scaling for Scene Generalization" summarizes tasks (5) The radar chart reports performance tasks (8)-(1)).
C.2 ADDITIONAL VISUAL GENERALIZATION UNDER OBJECT AND LOAD VARIATIONS
For completeness, we report the visual generalization experiment for Section 4.2. Here, we perturb all three loco-manipe manipulation tasks by modifying only the manipulated objects and their loads while keeping the scene layout fixed. Specifically. (i) in Bag Packing, one of the paper bags is replaced with another bag different appearance, size, and weight; (ii) Box Loading, the bags inside the box are replaced with plastic containers; and (iii) in Cart Pushing, the carton the cart is replaced with 60 k0 barbell plates. We evaluate WholeBodyVI.A, GROOT w/ LMO, and Open OFF- w/ LMO under these shifted conditions. As summarized in Fig. 6, WholeBodyVL. maintains the highest success rates across all perturbed settings, demonstrating that our framework remains robust when the manipulated objects and loads differ noticeably from those seen during training.
Table 6: Effect of removing proprioceptive state on the three tasks. Each task is decomposed into two subgoals and an averaged score. "WholeBody VLA w/o state" denotes variant where the robot state is not injected into the action decoder.
Bag Packing
Box Loading
Cart Pushing
Method
Score
Grasp Bags
Move & Squal
Squai & Grasp
Rise & Turn
Grab Handle
Push Ahead
WholeBody VLA w/o state
21/25
12/25 13/5
22/25 19/25
14/25 17/25
24/25 23/25
22/25 22/25
76.7%
WholeBedy
23/25
78.0%
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Deve Ner
Shorg Oreekiio
50
Fot +idh Tere
dembl
Rowfri
lccrosi dacies
Kobei pala
Krop Traadey
Fiainn fulore
La Chass
(a) Advance
(b) Sidestep slle
De Leyrechaaee
Whong
Locaoope Fatarr
Samar Qulkie
Deg Jheechabee
Plttel Wis Tabl
Fauu
20
Gewey
Lowentoon
Kulen Hii
Svowm
Whira Inpecce
Pacee//lee
11
ResigPales
Siiper
(c) Squat Bar
(d) Tumn
Figure 7: Failure modes of WholeBodyVLA in start-pose generalization. For each locomo- tion primitive-(a) advance, (b) sidestep, (c) squat, and (d) turn-we collect 50 failed trials from the start-pose generalization experiments (pick object and place into basket). Each Sankey dia- gram decomposes failures into locomotion vs. pick/place errors and finer-grained causes such as object/basket unreachable, wrong orientation, early stop, overshoot, collisions, stumble, and inaccu- rate grasp or placement.
Table 7: Effect of removing proprioceptive state under visual variations. Each task with visual variation (marked by ") is decomposed into two subgoals and an averaged score, using the perturba- tions described in Fig. 6.
Method
Bag Packing
Box Loading
Cart Pushing
Avg. Score
Grusp Bags
Move & Squat
Squat & Grasp
Rise & Turn
Grah Handle
Push Ahead
WholeBody VLA w/o state
12/25
5/25 9/25
14/25 15/25
12/25 15/25
21/25 22/25
21/25 20/25
76.7%
WholeBodyVLA
15/25
64.0%
To further probe whether WholeBody VLA truly relies on visual observations rather than the injected robot state, compare the full model with a variant that does not feed the proprioceptive state into the action decoder, both in the original setting (Table 6) and under the visual variations of Fig. 6 (Ta- ble 7). Removing the state input increases variance and slightly degrades performance, especially in the visually perturbed conditions, but the w/o-state variant still achieves comparable task comple- tion rates. This suggests that WholeBody VLA has indeed learned to accomplish maniuaaninl tasks primarily from visual input, and that the visual generalization observed in Section 4.5 does not hinge on access low-level proprioceptive information.
C.3 FAILURE MODE ANALYSIS
To gain insight into the remaining limitations of WholeBodyVL.A, we conduct a post-hoe failure analysis using the start-pose generalization experiments in Section 4.5. In this setting, the robot begins from diverse initial poses and must execute one approach primitive (advance, sidestep, squat, or turn) before picking object from the table and placing it into a basket. For each primitive gather 50 failed rollouts and manually annotate the underlying causes.
As illustrated in Fig. 7, failures first grouped into locomotion failures failures-where the robot ends in an unsuitable stance for picking or placing- and pick/place failures that occur despite a reasonable stance. Each is further decomposed into more fine-grained categories such as object unreachable
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Table 8: Average execuation duration (s) on the three tasks.
Bug Packing
Box Loading
Cart Pushing
Method
Grusp Bags
Move Squat
Squat & Grusp
Rise Turn
Grab Handle
Push Abead
Modular Design
19.2
23,0
21.5
7.2
12.0
11.7
GROOT LMO
26.3
38.6
21.1
19.5
13.8
OpenVLA-OFT LMO
33.2
16.9
WholeBody VLA (ours)
18.4
29.7
16.8
7.6
11.3
12.7
Human
Robot
Latent Action
Go Forward
Latent Action
Turn Left
Latent Action
Squat
Figure 8: Cross-domain retrieval with shared latent actions. Human and robot clips retrieved for the same latent action (e.g., go forward, turn left, squat) exhibit consistent semantics across domains.
(often caused by early stop. path deviation, overshoot, wrong orientation), basket unreachable, severe collision with the table or basket, stumble, or errors originating from the manipulation stage including wrong reaching trajectory, poor grasp pose, and misaligned placement.
Across the horizontal locomotion primitives (advance, sidestep, and turn), locomotion-related issues account for the majority of failures. Most unsuccessful episodes pass through the "object/basket un- reachable" node, indicating that moderate stance orientation deviations propagate into infeasible pick-and-place attempts. Severe collisions or stumbles occur much less frequently. In the squat primitive, failures are more evenly divided between locomotion (primarily incorrect final height or contact during descent) and pick/place errors caused by imprecise arm trajectories or grasp align- ment.
Although the analysis conducted within the start-pose generalization setup, the resulting patterns reflect broader limitations of the current model: the dominant failure modes arise from small but systematic stance and orientation errors during approach, rather than catastrophic behaviors. Im- proving approach precision-especially for turning, lateral stepping, and squatting expected to directly reduce downstream manipulation failures in future versions of the system.
C.4 TASK EXECUTION TIME
Besides success rates, we also report execution time, averaged over the five successful trials for each subgoal. For every successful trial, measure the wall-clock duration from the moment the instruction is issued to the moment the subgoal is judged successful. Table summarizes the average execution time aggregated over all subgoals within each task.
C.5 SHARED LATENT ACTION SPACE ACROSS EMBODIMENTS
Our three-stage training pipeline naturally leads to common latent action space for human and robot data. Stage trains VA-VAE LAMs as inverse dynamics models from purely visual videos: the encoder consumes (æz, T(+k) pairs and the discrete code summarizes the visual change between frames. Stage reuses the same videos and LAMs to supervise the VLA, which learns to predict these codes from images and language. Stage III then introduces Agibot X2 teleoperation data to
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associate latents with robot joint targets and locomotion commands. Because the codes depend only on Trame-to-frame visual motion rather than embodiment-speecife joint values, the same latent can align human and robot motions. Figure illustrates this behavior. For the latent action "Go Forward", the retrieved clips consistently include semantically aligned segments across both human- collected videos (left) and teleoperated rohot demonstrations (right). Despite coming from different sources, these clips all correspond to the same underlying latent action. Together with the strong gap between WholeBodyVIL. VLA and "w/o LAM" in Table 2, this supports that the learned latent space is shared and transferable.
C.6 SHARED LATENT ACTION SPACE ACROSS EMBODIMENTS
Apart from directly evaluating the downstream performance of the VLA, we introduce a conve- nient and efficient metric, the Relative Reconstruction Gain (RRG), to directly assess the quality of the LAM. Formally, we measure the reconstruction error the predicted future frame O against the ground-truth Oz+ MSE = MSE(O Ot+k). For comparison, define simple temporal baseline that estimates the later frame by directly copying the former, with error MSE MSE(o,, 0t4k). The proposed RRG is then given by:
MSEbase MSErecon
RRG
(6)
MSE
which captures the relative error reduction achieved by the LAM over the temporal baseline. higher RRG indicates that the LAM provides more predictive latent codes, serving efficient proxy for assessing LAM quality without relying solely on downstream policy evaluation.
We evaluate RRG on downstream task videos, shown in Table 9, Our recipe outperforms the shared one on both manipulation and locomotion splits, since the shared LAM underperforms the separate models on both manipulation and locomotion primitives. We consider this suggests inherent conflicts between locomotion speciffc and ceapppulation-speciii objectives when trained jointly.
Table 9: RRG (%) across three tasks. Each task is decomposed into primitives. Our separately trained LAMs show better downstream reconstruction performance than the shared alternative.
Bag Packing
Box Loading
Cart Pushing
Method
Grusp
Move
Squat
Place
Squat
Grasp
Rise
Turn
Place
Grab
manip. lam
21.78
23.64
18:71
24.73
21.22
25.09
22.92
28.15
23.96
19.12
19.92
shared lam
19.70
23.58
20.62
25.69
19.41
17.38
23.43
27.68
23.61
18.49
17.79
loco. lam
16.39
25.77
29.46
20.60
22.72
20.24
25.40
30.74
24.81
15.27
20.27
24


Reviews:
[{"review_id": "RsAlrYC2j2", "reviewer": "Reviewer_C1fR", "summary": "WholeBodyVLA addresses the challenge of enabling humanoid robots to perform large-space whole-body loco–manipulation, which requires tight integration of precise locomotion and dexterous manipulation. The authors identify two key limitations in prior work: (1) the scarcity of data that jointly captures manipulation-aware locomotion, and (2) the instability and imprecision of existing RL controllers due to continuous velocity-tracking objectives and unstructured upper-body perturbations. To overcome these, they propose a unified latent learning framework that trains separate Latent Action Models (LAMs) for manipulation and locomotion on low-cost, first-person videos, thereby enabling a vision-language-action (VLA) policy to jointly predict upper- and lower-body commands. For stable execution, they introduce a Loco–Manipulation–Oriented (LMO) RL policy featuring a discrete command interface (e.g., forward/turn/squat) and structured perturbations derived from real manipulation trajectories. Evaluated on the AgiBot X2 humanoid, WholeBodyVLA autonomously completes multi-step loco–manipulation tasks, including squatting to grasp a box, placing it onto a cart, and pushing a load exceeding 50 kg. The method outperforms strong end-to-end and modular baselines by 21.3% on average and demonstrates robust generalization under unseen visual conditions.", "questions": "**Questions**\n\n**Q1: Failure Mode Distribution** – In the reported 25×6 subgoal evaluation, what proportion of failures arises from each stage (approach, grasp, turn, squat, push)? Can the authors provide a quantitative breakdown and correlate it with LMO directional and stance-height errors to reveal whether locomotion deviations systematically cause manipulation failures?\n\n**Q2: Visual Generalization Boundary** – Beyond the tested appearance and load perturbations, has the system been evaluated under changes in ground friction, slope, lighting, occlusion, or dynamic obstacles? What are the success rates and degradation patterns under such environmental variations?\n\n**Suggestions**\n\n**S1: Detailed Failure Taxonomy** – Include a frequency breakdown of failures by task stage and relate them to quantitative LMO metrics (heading deviation, height tracking). This would help identify dominant failure patterns and clarify coupling between locomotion and manipulation.\n\n**S2: Broader Robustness Evaluation** – Extend experiments to cover terrain/friction variation, slopes, lighting/occlusion, and moving obstacles, providing explicit success curves or degradation trends to substantiate robustness beyond appearance shifts.\n\n**S3: Include standardized benchmarks and metrics** – Complement author-defined tasks with community-recognized scenarios and quantitative metrics (e.g., path/pose accuracy, time-to-goal, safety violations), enabling reproducible cross-paper comparison beyond the custom subgoal scores.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "**Originality**\n\nWholeBodyVLA introduces a unified vision-language-action framework for humanoid loco-manipulation, extending latent action learning from tabletop manipulation to full-body coordination. The work’s key novelty lies in the dual-LAM design, which explicitly separates locomotion and manipulation latent spaces to mitigate conflicts between camera ego-motion and hand–object motion—an insightful adaptation that broadens latent learning’s applicability. Additionally, the proposed Loco-Manipulation-Oriented (LMO) reinforcement learning scheme combines a discrete command interface and structured upper-body perturbations to achieve precision, start–stop control, and directional stability, marking a tailored optimization for humanoid whole-body control. While the system’s components (VQ-VAE, DINOv2, RL control) are not inherently new, their orchestration for large-space humanoid manipulation represents a meaningful, though evolutionary, advance with the first systematic real-world validation.\n\n**Quality**\n\nExperiments cover three multi-step real-world tasks with ablations on control precision, stability, and generalization under visual perturbations. While comprehensive, the analysis lacks deeper investigation into sample efficiency, LAM hyperparameter sensitivity, or scalability beyond ~50 teleoperated demos per task. The results validate feasibility but not robustness or data efficiency, limiting the strength of empirical claims. \n\n**Clarity**\n\nThe paper is well-structured and includes helpful runtime details (e.g., 10 Hz VLA, 50 Hz RL). However, critical parameters (codebook size, temporal stride k, reward weights) are buried in appendices, hindering reproducibility. Moreover, the interaction between locomotion and manipulation latents—central to the “unified” claim—remains underspecified, reducing interpretability.\n\n**Significance**\n\nThe work addresses a key challenge in humanoid autonomy: manipulation-aware locomotion in a unified perception-to-control pipeline. Its real-world demonstration of multi-step, heavy-load tasks offers tangible engineering significance. While not a conceptual leap, it represents an important integration step toward practical deployment.", "weaknesses": "**Insufficient Evidence for “Unified” Whole-Body Control**\n\nDespite the “unified” claim, the system decouples upper-body manipulation and lower-body locomotion: the VLA predicts separate manipulation and locomotion latents, a lightweight decoder outputs arm joint targets and a discrete locomotion command, and the LMO policy executes lower-body torques. This constitutes co-scheduled rather than end-to-end joint whole-body control, relying on downstream modules to handle cross-coupling.\n\n**Limited Generalization and Robustness Evaluation**\n\nVisual generalization is evaluated only under narrow appearance or load variations, such as unseen bags or cart weights. However, critical distribution shifts, including changes in scene layout, terrain friction, lighting, occlusion, or moving obstacles, are not tested, which limits the assessment of real-world robustness.\n\n**Non-standardized Tasks & Metrics Hinder Comparability**\n\nAll three tasks (bag packing, box loading, cart pushing) are custom-designed with ad-hoc subgoal scoring, lacking standardized benchmarks or community-agreed metrics. This hinders reproducibility and cross-method comparison.\n\n**No Failure Mode Analysis**\n\nTable 2 shows numerous subgoal-level failures across phases, yet the paper provides no breakdown of failure modes such as approach, grasp, turn, squat, or push to identify dominant bottlenecks.", "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": "BFYFJ3IC5E", "reviewer": "Reviewer_N1iB", "summary": "The paper proposes WholeBodyVLA, a unified VLA framework for end-to-end humanoid loco-manipulation on Agibot X2. To address data scarcity for manipulation-aware locomotion, the authors introduce unified latent learning with two VQ-VAE latent action models (separately for manipulation and locomotion). For precise and stable execution, a Loco–Manipulation–Oriented (LMO) RL controller with a discrete command interface is trained as the lower-body controller. Real-robot experiments on three suites (bag packing, box loading, cart pushing) are reported, where WholeBodyVLA achieves higher success rates than modular and open-source end-to-end baselines.", "questions": "1. Could you include real-world trials with ≥1 m initial separation (for example, robot stand 1m away from the table/cart) for all demos? Plots of success vs. start distance would help.\n2. For GR00T/OpenVLA baselines, can you fine-tune on the identical Agibot/LMO data? If already done, please add these details.\n3. Can you provide scaling curves for (a) total hours of action-free video for latent learning and (b) hours of robot/teleop data, and ablate the contribution of each?\n4. The current low-level LMO policy appears tuned for flat-ground walking and squatting motions. How does the approach generalize to more complex whole-body coordination tasks such as stair climbing, uneven terrain traversal, or heavy object lifting (which is common for loco-manipulation) that require significant upper–lower body coupling? If such generalization is not yet supported, could the authors discuss what architectural or training changes would be necessary to extend the controller to these regimes?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "1. Clear paper writing and problem formulation. The paper identifies the gap between modular pipelines and true end-to-end manipulation-aware locomotion, motivating why coupling is necessary for stability and task success.2. Unified latent learning with modality-separated LAMs. The paper identified the sub-optimality of using mixed-data to train a single LAM and propose to train separate LAMs for manip and locomotion\n2. Separation of latent action spaces. Training distinct latent action models for manipulation/locomotion is a sensible design choice that addresses mixed-motion dataset and improves learning for the high-level policy.\n3. Lower-body controller tailored for loco-manipulation. The LMO RL controller with a discrete command interface is well aligned with whole-body objectives (heading, position, stance), and the design is empirically validated.\n4. Real-robot evaluation. Experiments on physical hardware across multiple task families strengthen external validity and demonstrate the practicality of the method.", "weaknesses": "1. Ambiguity in loco-manipulation demands of the tasks. In all three demos, target objects appear within immediate arm’s reach at the start, so grasping could be completed without meaningful locomotion. Even though the side stepping motion is stable, it is still difficult to determine whether improvements arise from integrated loco-manipulation versus decoupled manipulation with stepping. \n2. Baseline fairness. Some baselines were not evidently fine-tuned on the same Agibot/LMO data or adapted to the same low-level interface. Since GR00T was originally trained with the data collected by NVIDIA's own low-level controller, directly plugging into LMO can be suboptimal or OOD. A stronger comparison would fine-tune GR00T/OpenVLA on the same teleop/robot dataset (of AgiBot) and interface, with matched training distribution.\n3. Limited analysis of latent-learning/data scaling. The paper motivates latent action learning as a solution for data scarcity, but there is no study of dataset size (with/without action labels) versus performance, nor a comparison of scaling first-person video versus teleop data. This limits conclusions about where latent learning delivers the biggest gains.", "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": "oIzGqjokHu", "reviewer": "Reviewer_76f3", "summary": "This paper introduces WholeBodyVLA, a unified Vision-Language-Action (VLA) framework that enables bipedal humanoid robots to perform large-space loco–manipulation tasks (e.g., squatting to grasp, turning to place, pushing 50 kg carts).\nThe method integrates two key innovations: 1. Unified Latent Learning (ULL) — jointly learns locomotion and manipulation latent actions from action-free egocentric videos. 2. Loco–Manipulation–Oriented (LMO) RL Policy — replaces conventional continuous velocity tracking with a discrete command interface and introduces structured upper-body perturbations from manipulation data to improve balance and stability. Experiments on the Agibot X2 humanoid show that WholeBodyVLA outperforms modular and end-to-end baselines (e.g., GR00T N1.5, OpenVLA-OFT) by 21.3–24 % in success rate, while maintaining robust multi-tasking and visual generalization under unseen conditions (e.g., different box contents or weights).", "questions": "See Weakness", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper clearly identifies the missing link between manipulation-aware locomotion and VLA-based manipulation, proposing the first end-to-end unified framework that integrates both in real-world humanoid control.\n2.\tThe discrete command interface + structured perturbation RL is elegantly engineered. The two-stage curriculum and well-defined reward shaping (directional accuracy, stand-still penalty) demonstrate deep insight into locomotion stability and precision.\n3.\tEvaluation on multiple real-world tasks (bag packing, box loading, cart pushing) and ablations (without RL, with velocity-based RL, with shared LAM, etc.) show both component-wise contributions and robustness. The use of both real-robot and MuJoCo simulations strengthens credibility.", "weaknesses": "1.\tThe upper-body movements demonstrated in the three tasks appear quite limited — the shoulder seems to move primarily along the pitch axis. However, since the tabletop objects can be positioned in more diverse locations, introducing richer interaction motions could better showcase the capabilities of the VLA.\n2.\tThe current tabletop task is rather simple and singular. Did the authors also collect additional related data that could be used to supplement or expand the current manipulation tasks?", "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": "OBqCPLp6uC", "reviewer": "Reviewer_1asV", "summary": "This paper presents WholeBodyVLA, a unified framework for humanoid loco-manipulation. The framework consists of two main components: Unified Latent Learning and the Loco-Manipulation-Oriented (LMO) RL Policy. In Unified Latent Learning, two separate VQ-VAE–based latent models are trained on manipulation and locomotion videos. The LMO policy is a reinforcement learning controller that executes discrete locomotion commands and adopts a two-stage curriculum to enhance training stability and precision. During inference, the VLA model decodes latent actions into upper-body joint angles and locomotion commands, which the LMO policy translates into torques for the robot’s lower body. Experiments conducted on the AgiBot X2 humanoid across three real-world loco-manipulation tasks demonstrate that WholeBodyVLA outperforms both modular and end-to-end baselines.", "questions": "- Is my understanding correct that the LAMs are trained using human egocentric videos, while the VLA (or VLM) is trained using teleoperation data? If so, could the authors clarify why and how human and robot data can share the same latent action space?\n\n- Would it be possible to pretrain the LAM using the teleoperation dataset’s camera videos instead of (or in addition to) human egocentric data? This might provide a more consistent distribution between perception and control domains.\n\n- Regarding the data scarcity issue, is the problem primarily addressed by pretraining the LAM on large-scale human video data to provide latent supervision for robot policy learning? Please confirm if this interpretation is correct.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper addresses an interesting and timely topic in humanoid robot control, focusing on whole-body loco-manipulation, which is of high relevance to the embodied AI and robotics communities.\n\n- The proposed method is conceptually straightforward and well structured, making it relatively easy to reproduce and extend in future research.\n\n- The experimental results demonstrate that the proposed method outperforms several strong baselines, indicating the effectiveness and practicality of the approach.\n\n- The paper includes comprehensive ablation experiments that provide necessary insights into the contribution of each component.", "weaknesses": "The paper could benefit from clearer elaboration and justification of certain key arguments:\n- The authors identify data scarcity and instability in RL controllers as the two main challenges in humanoid loco-manipulation. However, the connection between data scarcity and degraded loco-manipulation performance is not sufficiently explained or empirically verified. It would be helpful to clarify how the lack of data leads to poor generalization and to provide stronger evidence that the proposed use of video-based learning effectively mitigates this limitation.\n- Some sections of the method description could be written more clearly, particularly regarding model training details and implementation specifics (see “Questions” section for further comments).\n\nThe locomotion component is relatively simple, relying on a small set of discrete motion primitives. This limits the expressiveness and adaptability of the policy for more complex movements.\n\nThe experimental evaluation could be improved by including longer-horizon tasks that require extended locomotion or sequential coordination, which would better demonstrate the robustness and scalability of the proposed framework.", "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)