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arxiv: 2605.30503 · v1 · pith:ZXCC3Y3Qnew · submitted 2026-05-28 · 💻 cs.RO · cs.SY· eess.SY· stat.ML

Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

Vittorio Giammarino , Anastasios Manganaris , Ahmed H. Qureshi This is my paper

Pith reviewed 2026-06-29 06:49 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SYstat.ML
keywords physics-informed goal-conditioned reinforcement learninghybrid contact dynamicscontact-rich manipulationgoal-conditioned RLhybrid dynamicsrobotic manipulationreinforcement learning
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The pith

Structural properties of contact interactions cause existing physics-informed goal-conditioned RL methods to degrade in manipulation, which contact-aware and hierarchical formulations address by applying biases selectively.

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

The paper establishes that contact interactions create hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes. These features cause standard physics-informed goal-conditioned reinforcement learning to degrade when applied directly to contact-rich manipulation tasks. The authors develop contact-aware and hierarchical formulations that apply the inductive biases only in appropriate parts of the problem. This matters for extending goal-conditioned learning from sparse rewards to versatile robotic manipulation, which has so far been limited to simpler domains without contacts.

Core claim

Contact interactions induce hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes that cause existing Pi-GCRL methods to degrade when applied naively to contact-rich manipulation. Motivated by this analysis, contact-aware and hierarchical formulations apply physics-informed inductive biases selectively across the manipulation problem, providing a principled step toward extending Pi-GCRL to contact-rich manipulation.

What carries the argument

Contact-aware and hierarchical formulations that apply physics-informed inductive biases selectively across the manipulation problem to handle hybrid contact dynamics.

If this is right

  • Existing Pi-GCRL methods degrade on contact-rich manipulation due to hybrid dynamics and nonsmooth landscapes.
  • Contact-aware formulations apply physics-informed biases only where they remain valid.
  • Hierarchical formulations handle mode switches and controllability changes across contact modes.
  • The approach extends reliable goal-conditioned learning from navigation domains to contact-rich robotic tasks.
  • Selective application of inductive biases becomes necessary for problems with nonsmooth value landscapes.

Where Pith is reading between the lines

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

  • The selective-bias pattern could apply to other hybrid systems such as legged locomotion or multi-body assembly.
  • Integration with explicit contact-mode detection might further stabilize value learning in these settings.
  • Real-robot experiments would test whether the hierarchical split reduces sample complexity compared with flat formulations.

Load-bearing premise

Contact interactions induce hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes that directly cause degradation in existing Pi-GCRL methods.

What would settle it

Experiments on contact-rich manipulation tasks that show no performance degradation when using existing Pi-GCRL methods or no improvement when using the contact-aware hierarchical formulations.

Figures

Figures reproduced from arXiv: 2605.30503 by Ahmed H. Qureshi, Anastasios Manganaris, Vittorio Giammarino.

Figure 1
Figure 1. Figure 1: Results for regressing d ∗ in (5). In the no-contact mode, full-state Eikonal regularization distorts the learned function dθ due to locally uncontrollable coordinates, as highlighted in Proposi￾tion 4.1. The HJB regularizer addresses this issue by constraining only the controllable direction. In the holding mode, where all coordinates are locally controllable, all losses recover similar solutions. (a) Dou… view at source ↗
Figure 2
Figure 2. Figure 2: OGBench [43] manipulation environ￾ments used in our experiments. The state includes robot proprioception, end-effector pose, gripper state, and object poses, and the goal is to arrange the cubes into a specified target configuration. We organize our experiments around four main questions: (1) does full-state Eikonal regular￾ization distort value learning when some co￾ordinates are locally uncontrollable? (… view at source ↗
Figure 3
Figure 3. Figure 3: Results on the OGBench cube environments. The notation in each subplot title, e.g., [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: State representation ablation for hierarchical algorithms. For each algorithm, representation, [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Real-world experimental setup. The task consists of moving an object to the center of the table from randomized initial poses. Real-world experiments. We further evalu￾ate the proposed framework on the real-world pick-and-place task shown in [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: 1-D toy example of a contact-rich manipulation task with mode-dependent dynamics. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Extended visualization of the hybrid toy example in Fig. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: State representation ablation. We compare hierarchical algorithms using the full manip [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Data collection on the real-world pick-and-place setup. The task is performed with a UR5e [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative examples of real-world rollouts collected from the learned policy. Each row [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
read the original abstract

Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned reinforcement learning (GCRL) tackles this challenge by learning policies that generalize across goals, but this generalization becomes increasingly difficult as the underlying dynamics become high-dimensional, hybrid, or contact-dependent. To address this issue, physics-informed GCRL (Pi-GCRL) introduces optimal-control-inspired inductive biases into goal-conditioned value learning. While Pi-GCRL methods have proven effective in navigation and object-free goal-reaching domains, their reliability in contact-rich tasks remains unclear, where contact interactions induce hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes. In this work, we show that these structural properties can cause existing Pi-GCRL methods to degrade when applied naively to contact-rich manipulation. Motivated by this analysis, we introduce contact-aware and hierarchical formulations that apply physics-informed inductive biases selectively across the manipulation problem. Our results provide a principled step toward extending Pi-GCRL to contact-rich manipulation.

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

1 major / 0 minor

Summary. The manuscript claims that structural properties of contact-rich manipulation—hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes—cause existing physics-informed goal-conditioned RL (Pi-GCRL) methods to degrade when applied naively. Motivated by this analysis, it introduces contact-aware and hierarchical formulations that apply physics-informed inductive biases selectively across the manipulation problem.

Significance. If the claimed degradation is demonstrated and the new formulations are shown to mitigate it, the work would address a relevant gap in extending Pi-GCRL to realistic robotic contact tasks. The abstract, however, supplies no equations, derivations, experiments, or data, so the significance cannot be assessed from the provided text.

major comments (1)
  1. [Abstract] Abstract: the central claim that 'these structural properties can cause existing Pi-GCRL methods to degrade' is asserted without any supporting equations, experimental results, error bars, or data; the claim therefore cannot be verified or stress-tested for internal consistency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'these structural properties can cause existing Pi-GCRL methods to degrade' is asserted without any supporting equations, experimental results, error bars, or data; the claim therefore cannot be verified or stress-tested for internal consistency.

    Authors: We agree that the abstract, being a concise summary, does not contain the supporting equations, derivations, or experimental data. The full manuscript supplies this material: Section 3 analyzes the structural properties (hybrid dynamics, mode-dependent controllability, nonsmooth value landscapes) and their effect on Pi-GCRL; Sections 4–5 provide the theoretical arguments and empirical results (including error bars) showing degradation on contact-rich tasks and the benefit of the contact-aware and hierarchical formulations. To address the concern, we will revise the abstract to briefly reference the supporting analysis and results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and provided text contain no equations, derivations, fitted parameters, or self-citations that form a load-bearing chain. Claims about structural properties causing degradation in Pi-GCRL methods and the introduction of contact-aware formulations are stated at a high level without any reduction to inputs by construction or renaming of known results. The derivation chain cannot be walked because no technical steps, proofs, or predictive claims are exhibited; this is the normal case of a self-contained high-level motivation with no internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, axioms or invented entities; ledger left empty.

pith-pipeline@v0.9.1-grok · 5729 in / 883 out tokens · 27007 ms · 2026-06-29T06:49:24.574524+00:00 · methodology

discussion (0)

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