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arxiv: 2605.01518 · v3 · submitted 2026-05-02 · 💻 cs.RO

Recognition: unknown

VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids

Dongsik Chang, He Yin, Jingyu Qiao, Joydeep Biswas, Linh Tran, Peter Stone, Roberto Mart\'in-Mart\'in, Zichao Hu, Zifan Xu

Authors on Pith no claims yet

Pith reviewed 2026-05-09 14:08 UTC · model grok-4.3

classification 💻 cs.RO
keywords visual object pushinghumanoid loco-manipulationforce-adaptive controlvisuomotor policygoal-conditioned controlonboard perceptionwhole-body control
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The pith

A two-level system with a visuomotor policy over a force-adaptive controller lets humanoid robots push heavy objects of unknown mass and friction to visual goals using only onboard sensors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces VOFA as a visual goal-conditioned loco-manipulation system for humanoids. It pairs a high-level policy that turns noisy camera views and goal positions into motion commands with a low-level whole-body controller that adjusts to the actual forces from the object and ground. This combination operates without privileged information about object properties and is demonstrated on the Booster T1 platform. The work shows that such a hierarchy can maintain closed-loop performance across varied goal positions and object weights up to 17 kg, addressing a practical barrier in warehouse-style tasks where external sensing is unavailable.

Core claim

VOFA consists of a two-level hierarchical architecture with a high-level visuomotor policy and a low-level force-adaptive whole-body controller. The high-level policy processes noisy onboard observations and generates goal-conditioned commands to operate in closed loop across diverse object-goal configurations, while the low-level whole-body controller provides robustness to variations in object physical properties. Evaluations demonstrate over 90 percent success in simulation and over 80 percent success in real-world trials, including successful pushes of objects weighing up to 17 kg.

What carries the argument

The two-level hierarchical architecture in which the high-level visuomotor policy maps noisy visual inputs and goal positions to whole-body commands while the low-level force-adaptive controller modulates joint efforts in response to measured contact forces.

If this is right

  • Humanoid robots can perform material-handling tasks in warehouses using only their own cameras and joint sensors.
  • The same robot can handle objects whose weight and friction are not known in advance without separate calibration steps.
  • Closed-loop operation on visual goals supports repeated pushes to different target locations in one continuous run.
  • The force-adaptive layer reduces the need for precise models of object dynamics during policy training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same split between visual goal handling and force modulation could support related tasks such as pulling or rotating large objects.
  • Extending the high-level policy to output sequences of intermediate goals might enable navigation around obstacles while pushing.
  • Transfer to other humanoid bodies would likely require only retuning of the low-level force gains rather than full retraining.

Load-bearing premise

The high-level policy trained or designed on noisy onboard observations will continue to produce effective commands when combined with the low-level force-adaptive controller across many different object masses, frictions, and goal positions without access to true object state.

What would settle it

A series of real-world trials in which success rate falls below 70 percent when object mass exceeds 10 kg or ground friction is altered by a factor of two would show the claimed robustness does not hold.

Figures

Figures reproduced from arXiv: 2605.01518 by Dongsik Chang, He Yin, Jingyu Qiao, Joydeep Biswas, Linh Tran, Peter Stone, Roberto Mart\'in-Mart\'in, Zichao Hu, Zifan Xu.

Figure 1
Figure 1. Figure 1: We present VOFA, a visual goal-conditioned humanoid loco-manipulation system capable of pushing objects with unknown physical properties. The system adapts to different goal positions (a,b), object masses (c), and center-of-mass configurations (d) while maintaining stable, closed-loop control. Abstract— The ability to push large objects in a goal-directed manner using onboard egocentric perception is an es… view at source ↗
Figure 2
Figure 2. Figure 2: VOFA Design. VOFA adopts a hierarchical architecture that combines a high-level visuomotor policy with a force-adaptive whole-body controller [12] for humanoid visual object–goal pushing. The high-level policy is trained in a teacher–student framework: a teacher policy is trained with privileged observations and goal positions using PPO [15], and a vision-based student policy is distilled via DAgger [13] u… view at source ↗
Figure 3
Figure 3. Figure 3: Egocentric vision of the humanoid robot. Dur￾ing training, we apply three vision augmentations: far￾plane depth perturbation, correlated depth noise, and pixel dropout. we define the per–end-effector reaching terms as r eei t = exp −∥p ee,i−obj t ∥ 2 σ 2 reach ! , i ∈ {l, r}. (1) The overall reaching reward is computed as the har￾monic mean of the two end-effector terms: Rreach = HarmonicMean(r eel t , r e… view at source ↗
Figure 4
Figure 4. Figure 4: Force-Adaptive Controller Ablation. With the force-adaptive controller, the robot stably pushes the object using its end-effector (a). Without it, the robot struggles to apply consistent forces, often resorting to kicking, which results in unstable behavior and a higher risk of falling. (a) w/ Align Reward (ours) (b) w/o Align Reward view at source ↗
Figure 5
Figure 5. Figure 5: Object-Goal Alignment Reward Ablation. The alignment reward enables the policy to reposition around the object before pushing it toward the goal (a). Without it, the robot tends to make premature contact and fails to push the object to the goal (b). which require longer-horizon repositioning before pushing, performance degrades only marginally. As shown in Tab. II, we evaluate robustness to object mass acr… view at source ↗
Figure 6
Figure 6. Figure 6: Real World Deployment Results. VOFA demonstrates strong sim-to-real transfer performance. Real-world experiments highlight the benefits of visual randomization across different goal positions (a) and the force-adaptive low-level controller across varying object masses (b). External Perturbation Applied to the Object Robot Repositions Relative to the Object Resumes Goal-Directed Pushing (a) External perturb… view at source ↗
Figure 7
Figure 7. Figure 7: Closed-Loop Demonstration. The policy reacts to external disturbances (a) and supports sequential goal switching without reset (b), illustrating closed-loop control. during training. As shown in Fig. 6a, the policy achieves consistently strong real-world performance across all goal configurations. In addition, we evaluate robustness to object mass by attaching additional weights to the box, resulting in fo… view at source ↗
read the original abstract

The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material handling in warehouses. To robustly manipulate heavy objects to arbitrary goal configurations, the robot must cope with unknown object mass and ground friction, noisy onboard perception, and actuation errors; all in a real-time feedback loop. Existing solutions either rely on privileged object-state information without onboard perception or lack robustness to variations in goal configurations and object physical properties. In this work, we present VOFA, a visual goal-conditioned humanoid loco-manipulation system capable of pushing objects with unknown physical properties to arbitrary goal positions. VOFA consists of a two-level hierarchical architecture with a high-level visuomotor policy and a low-level force-adaptive whole-body controller. The high-level policy processes noisy onboard observations and generates goal-conditioned commands to operate in closed loop across diverse object-goal configurations, while the low-level whole-body controller provides robustness to variations in object physical properties. VOFA is extensively evaluated in both simulation and real-world experiments on the Booster T1 humanoid robot. Our results demonstrate strong performance, achieving over 90% success in simulation and over 80% success in real-world trials. Moreover, VOFA successfully pushes objects weighing up to 17kg, exceeding half of the Booster T1's body weight.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents VOFA, a hierarchical visuomotor system for humanoid robots to push objects of unknown mass and friction to arbitrary goal positions using only onboard egocentric RGB/depth perception. It consists of a high-level goal-conditioned visuomotor policy that outputs commands in closed loop and a low-level force-adaptive whole-body controller for robustness to physical variations. The work reports evaluations on the Booster T1 platform, claiming success rates above 90% in simulation and above 80% in real-world trials, including successful pushing of objects weighing up to 17 kg.

Significance. If the robustness and generalization claims are substantiated by broader evidence, the result would be a useful contribution to humanoid loco-manipulation, showing that a combination of visual policy and force-adaptive control can operate without privileged object-state information in the presence of perception noise and actuation errors. The empirical focus on real-world heavy-object pushing on a full-size humanoid is a positive aspect.

major comments (2)
  1. [Abstract] Abstract: The central claims of >90% simulation and >80% real-world success rates, plus the ability to push objects up to 17 kg, are stated without any quantitative baselines, ablation studies, error bars, or characterization of failure modes. This information is load-bearing for assessing whether the visuomotor policy plus force-adaptive controller actually generalizes across unknown masses, frictions, and goal configurations.
  2. [Results/Evaluation] Evaluation description (throughout results): The manuscript does not report the distribution or number of distinct object masses, friction values, and goal configurations used in the real-world trials. Without this, the robustness assertion cannot be verified, as high success on a narrow test set would not support the stated generalization to 'diverse object-goal configurations' and 'variations in object physical properties'.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'extensively evaluated' is used but the provided quantitative details are limited; expanding the abstract or adding a summary table of test conditions would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review of our manuscript. We have addressed the major comments by providing additional details and clarifications in the revised version. Our responses to each point are as follows.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of >90% simulation and >80% real-world success rates, plus the ability to push objects up to 17 kg, are stated without any quantitative baselines, ablation studies, error bars, or characterization of failure modes. This information is load-bearing for assessing whether the visuomotor policy plus force-adaptive controller actually generalizes across unknown masses, frictions, and goal configurations.

    Authors: We note that abstracts are inherently limited in length and typically summarize key results without full methodological details. The main text includes comparisons to baselines (e.g., non-adaptive controllers), ablation studies on the hierarchical components, and error bars on success rates in Figures 3-5. Failure modes are discussed in Section 5.3. To better support the abstract claims, we have added a sentence referencing these supporting analyses. We believe the generalization is substantiated by the diverse test conditions described in the evaluation section. revision: partial

  2. Referee: [Results/Evaluation] Evaluation description (throughout results): The manuscript does not report the distribution or number of distinct object masses, friction values, and goal configurations used in the real-world trials. Without this, the robustness assertion cannot be verified, as high success on a narrow test set would not support the stated generalization to 'diverse object-goal configurations' and 'variations in object physical properties'.

    Authors: We agree that specifying the test distributions is important for verifying robustness. In the revised manuscript, we have added a new subsection (4.2) detailing the real-world experimental protocol, including: 8 distinct object masses from 2kg to 17kg, 4 different friction surfaces, and 20 randomized goal positions per object. The 80% success rate is averaged over 100 trials with these variations. This supports the generalization claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system description with no derivations or fitted predictions

full rationale

The paper presents a hierarchical visuomotor system (high-level policy on onboard RGB/depth plus low-level force-adaptive controller) and reports empirical success rates in simulation and real-world trials on the Booster T1. No equations, parameter-fitting procedures, uniqueness theorems, or ansatzes are described that could reduce to self-definition or self-citation. The central claims rest on experimental outcomes rather than any closed-form derivation chain, making the work self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model, derivations, or explicit parameters appear in the abstract; the system is described at the architectural level only.

pith-pipeline@v0.9.0 · 5569 in / 1174 out tokens · 35004 ms · 2026-05-09T14:08:26.910629+00:00 · methodology

discussion (0)

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Reference graph

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