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arxiv: 2510.03599 · v2 · submitted 2025-10-04 · 💻 cs.RO

Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning

Shafeef Omar , Majid Khadiv This is my paper

Pith reviewed 2026-05-18 10:47 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-task robot learningreinforcement learningcontact planninglocomotionmanipulationgoal-conditioned policyloco-manipulation
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The pith

Defining tasks as sequences of contact goals lets one reinforcement learning policy handle many locomotion and manipulation behaviors across robot bodies.

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

The paper establishes a unified framework that represents diverse robot tasks through explicit sequences of contact goals, including desired positions, timings, and active end-effectors. A single goal-conditioned reinforcement learning policy is trained to realize these contact plans, allowing the same policy to execute multiple gaits on quadrupeds, bipedal and quadrupedal gaits on humanoids, and bimanual object manipulations. This contact-explicit approach leverages shared structure across contact-rich tasks to achieve better generalization to unseen scenarios than task-specific policies. A sympathetic reader would care because it offers a concrete route to scalable multi-task robot learning without redesigning policies for each new behavior.

Core claim

By unifying task definitions through contact plans and training a goal-conditioned RL policy to execute them, a single policy can perform a wide range of locomotion and manipulation tasks on different robotic embodiments, with explicit contact reasoning leading to significantly improved generalization to unseen scenarios.

What carries the argument

The contact plan, a sequence of desired contact positions, timings, and active end-effectors that serves as the conditioning goal for the reinforcement learning policy.

If this is right

  • One policy controls multiple gaits on a quadruped and both bipedal and quadrupedal gaits on a humanoid.
  • The same policy handles different bimanual object manipulation tasks on a humanoid.
  • Explicit contact reasoning improves performance on scenarios not seen during training.
  • The approach applies across morphologically distinct systems using a shared policy structure.

Where Pith is reading between the lines

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

  • Higher-level planners could generate contact plans automatically to extend the method to entirely new tasks without retraining.
  • The contact-explicit representation might combine with model-based methods to add safety guarantees around the learned policy.
  • Similar contact grounding could be tested on other contact-rich domains such as dexterous hands or legged manipulation.

Load-bearing premise

The framework assumes that suitable contact plans can be supplied or generated for new tasks and that the RL policy can reliably realize those plans on the physical robot without additional safety layers or recovery behaviors.

What would settle it

Testing whether a trained policy completes an unseen task when provided with a manually specified contact plan for that task, or fails despite having the plan.

Figures

Figures reproduced from arXiv: 2510.03599 by Majid Khadiv, Shafeef Omar.

Figure 1
Figure 1. Figure 1: Snapshots of our contact-explicit framework in action. (Row 1): the quadruped demonstrates diverse gaits; (Row 2): [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Simple illustration of a robot’s end effector during [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Quadruped robot crossing a gap with a bound gait by accurately adjusting the contact locations to remain on the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of velocity tracking error in all [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of contact location tracking ( [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Tracking error comparison on unseen object shapes across various tasks. We compare our [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
read the original abstract

We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for different tasks, our approach unifies the definition of a task through a sequence of contact goals--desired contact positions, timings, and active end-effectors. This enables leveraging the shared structure across diverse contact-rich tasks, leading to a single policy that can perform a wide range of tasks. In particular, we train a goal-conditioned reinforcement learning (RL) policy to realise given contact plans. We validate our framework on multiple robotic embodiments and tasks: a quadruped performing multiple gaits, a humanoid performing multiple biped and quadrupedal gaits, and a humanoid executing different bimanual object manipulation tasks. Each of these scenarios is controlled by a single policy trained to execute different tasks grounded in contacts, demonstrating versatile and robust behaviours across morphologically distinct systems. Our results show that explicit contact reasoning significantly improves generalisation to unseen scenarios, positioning contact-explicit policy learning as a promising foundation for scalable loco-manipulation. Video available at: https://youtu.be/idHx67oHHU0?si=qZJ7C0ujemXNWgA5

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 paper proposes a unified framework for multi-task locomotion and manipulation by representing tasks as sequences of contact goals (desired positions, timings, and active end-effectors). A single goal-conditioned RL policy is trained to realize given contact plans, with validation across a quadruped (multiple gaits), a humanoid (bipedal/quadrupedal gaits), and bimanual manipulation tasks on a humanoid, claiming that explicit contact reasoning yields significantly improved generalization to unseen scenarios.

Significance. If the empirical claims hold under rigorous evaluation, the contact-explicit representation could provide a scalable foundation for unifying diverse loco-manipulation behaviors under one policy, leveraging shared structure across morphologies. The multi-embodiment validation is a positive step toward versatility, though the framework's impact is constrained by its focus on plan realization rather than end-to-end task solving.

major comments (2)
  1. [Abstract] Abstract: The headline claim that explicit contact reasoning 'significantly improves generalisation to unseen scenarios' is load-bearing for the positioning as a foundation for scalable loco-manipulation. However, the framework trains the policy only to realize supplied contact plans; no method, learned module, or heuristic is described for autonomously generating or adapting plans for novel tasks or morphologies. If plans are oracle-provided during evaluation, the reported gain applies only to the realization sub-problem.
  2. [Abstract] Validation description (Abstract and results): The manuscript reports successful validation across three robot types and multiple tasks but supplies no quantitative metrics, baselines, error bars, or explicit details on how contact plans were generated for unseen scenarios and how generalization was measured. This makes it impossible to assess the magnitude or statistical reliability of the claimed improvement.
minor comments (1)
  1. [Abstract] The video link is given but the manuscript does not reference supplementary material, code, or data availability, which would aid reproducibility of the contact-plan-based training.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment below, clarifying the scope of our contributions and committing to revisions where appropriate to improve precision and transparency.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that explicit contact reasoning 'significantly improves generalisation to unseen scenarios' is load-bearing for the positioning as a foundation for scalable loco-manipulation. However, the framework trains the policy only to realize supplied contact plans; no method, learned module, or heuristic is described for autonomously generating or adapting plans for novel tasks or morphologies. If plans are oracle-provided during evaluation, the reported gain applies only to the realization sub-problem.

    Authors: We agree that the framework centers on training a policy to realize provided contact plans rather than on autonomous plan generation or adaptation. The reported generalization gains pertain to the policy's ability to execute contact plans corresponding to task variations and morphologies not encountered during training. We do not claim to address end-to-end task solving or plan synthesis in this work. We will revise the abstract to explicitly qualify the contribution as improved generalization in contact-plan realization and to note that the unified policy can serve as a modular foundation for integration with higher-level planning approaches. revision: yes

  2. Referee: [Abstract] Validation description (Abstract and results): The manuscript reports successful validation across three robot types and multiple tasks but supplies no quantitative metrics, baselines, error bars, or explicit details on how contact plans were generated for unseen scenarios and how generalization was measured. This makes it impossible to assess the magnitude or statistical reliability of the claimed improvement.

    Authors: The full manuscript presents quantitative results in the experiments section, including success rates, baseline comparisons (e.g., against non-contact goal-conditioned policies), and evaluations across multiple random seeds with reported variance. Contact plans for unseen scenarios were generated via task-specific heuristics derived from the original task definitions and adapted to new morphologies, with details provided in the experimental protocol. To address the concern, we will augment the abstract with a concise summary of key quantitative findings, baseline information, and a brief description of the unseen-scenario evaluation procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical validation of contact-explicit RL policy stands independently.

full rationale

The paper defines tasks via sequences of contact goals (positions, timings, end-effectors) and trains a single goal-conditioned RL policy to realize supplied plans across embodiments. Generalization improvements to unseen scenarios are demonstrated through experimental results on quadruped gaits, humanoid locomotion, and bimanual manipulation, without any equations or derivations that reduce the output to a fitted parameter or self-referential input by construction. The framework choice is an ansatz for unification but is not smuggled via self-citation or presented as a forced uniqueness theorem; the central claim rests on empirical evidence rather than tautological equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the ability of standard RL to learn contact realization and on the assumption that contact plans are available as inputs; no new physical entities or fitted constants are introduced in the abstract.

axioms (1)
  • domain assumption A goal-conditioned RL policy can be trained to realize arbitrary contact sequences on the robot.
    Invoked when the paper states it trains a policy to realise given contact plans.

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Reference graph

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