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arxiv: 2606.28196 · v1 · pith:LFSTXYDDnew · submitted 2026-06-26 · 💻 cs.RO

Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space

Ha Thang Long Doan , Hikaru Arita , Kazuto Nakashima , Kenji Tahara This is my paper

Pith reviewed 2026-06-29 04:11 UTC · model grok-4.3

classification 💻 cs.RO
keywords in-grasp manipulationdexterous manipulationreinforcement learningstable graspnon-dropping action spacephysics-guided learningskill decomposition
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The pith

Decomposing in-grasp manipulation into theory-constrained components makes reinforcement learning efficient and stable for repositioning without dropping.

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

End-to-end reinforcement learning for dexterous in-grasp tasks often encounters instability and conflicting objectives that slow or prevent learning of stable grasps. This paper shows that splitting the overall skill into simpler components, each shaped by constraints from classical physics and control theory, removes those conflicts. The resulting controller learns to maintain a stable grasp while repositioning or reorienting objects inside the hand. The same decomposition produces reliable performance across changes in object shape, sensor noise, motor noise, latency, and surface friction.

Core claim

By decomposing dexterous skills into multiple simpler and analyzable components and learning each component with explicit constraints and guidance drawn from classical physics and control theory, the acquisition of stable grasp maintenance together with in-grasp reposition and reorientation becomes both efficient and stable, even when objects, sensor and motor noise, communication latency, and frictional conditions vary.

What carries the argument

Decomposition of the full manipulation skill into simpler components, each constrained by classical physics and control theory, inside a non-dropping action space that prevents object release.

If this is right

  • Stable in-grasp repositioning and reorientation become learnable for multiple object geometries without hand-specific analytic models.
  • The learned skills remain functional under realistic sensor noise, motor noise, and communication delays.
  • Friction variations between object and finger surfaces no longer require separate retuning of the controller.
  • The non-dropping constraint built into the action space keeps the object in the hand throughout learning and execution.

Where Pith is reading between the lines

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

  • The same decomposition pattern could be applied to other contact-rich tasks such as in-hand tool use or precise assembly.
  • The non-dropping action space may serve as a reusable safety layer when combining learned skills with model-based planners.
  • Because each component stays analyzable, the approach may allow incremental addition of new sub-skills without retraining the entire policy.

Load-bearing premise

That the instability and objective conflicts seen in end-to-end RL arise mainly from the lack of explicit physical constraints on each sub-skill and can therefore be removed by decomposition.

What would settle it

A controlled experiment in which an unconstrained end-to-end RL agent is trained on the same in-grasp repositioning task with added latency and friction variation and is shown to drop the object or fail to converge while the decomposed version succeeds within the same number of trials.

Figures

Figures reproduced from arXiv: 2606.28196 by Ha Thang Long Doan, Hikaru Arita, Kazuto Nakashima, Kenji Tahara.

Figure 1
Figure 1. Figure 1: FTODG control : (a) Force/torque equilibrium achieves object stability, while (b) feedback control error guides the fingertip force/moment for stable reposition/reorientation. Reposition: Let Xd be the desired position, the current VF position be XVF, and the real object position be Xobject. Then, the feedback control signals upos i for each finger i to stably minimize the position error epos are straightf… view at source ↗
Figure 2
Figure 2. Figure 2: Stable action space for in-grasp manipulation learning. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: TSIGL framework: An RL policy learns stable manip [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Skill 1: (a) External forces from 0.1 N to 8.6 N applied to objects under force/torque equilibrium and (b) displacement of a cuboid when 4.6 N is applied from 0.58 s to 1.25 s [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Learning Skill 2: (a) Our TSIGL framework learning in-grasp position manipulation skill, (b) sample efficiency comparison with other soft constraint techniques, and (c) number of times an object is dropped during RL. 2) CBFs are Necessary to Constrain the Stable Action Space: An ablation study of the TSIGL framework is con￾ducted during Skill 2 learning. We study the effect of softening the implementation … view at source ↗
Figure 6
Figure 6. Figure 6: Unstable skills when (a) softening the implementation [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Learning Skill 3: (a) Our TSIGL framework for learning in-grasp orientation manipulation skill; (b) main task, and (c) simplified task’s RL performance compared to other learning methods. • End-to-end learning: The environment with gravity reaches 0.8 consecutive manipulation successes (shown as a dark blue line in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average consecutive successes when FTODG’s con [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
read the original abstract

Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects being manipulated. Reinforcement learning (RL) has become another common approach in which skills are explored openly in an end-to-end manner but is inefficient because of unnoticeable instability and conflicts in learning objectives. This paper attempts to efficiently explore stable and accurate manipulation skills by decomposing dexterous skills into multiple simpler/analyzable components. Each skill component is subsequently learned with constraints and guidance from classical physics and control theory. Our work shows that for stable grasp, in-grasp reposition/reorientation with different objects, sensor/motor noise, latency, and frictional conditions, skill learning becomes efficient and stable with prior knowledge from theory.

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 argues that analytic dexterous manipulation controllers rely on strong assumptions while end-to-end RL suffers from instability and objective conflicts; it proposes decomposing skills into simpler components each constrained by classical physics and control theory, claiming this yields efficient and stable learning for stable grasp and in-grasp reposition/reorientation across objects, sensor/motor noise, latency, and friction.

Significance. If the empirical results hold, the hybrid decomposition could reduce sample inefficiency in RL for dexterous tasks by injecting domain knowledge, offering a practical bridge between model-based control and learning-based methods.

major comments (1)
  1. Abstract: the central empirical claim that 'skill learning becomes efficient and stable with prior knowledge from theory' is asserted without any quantitative results, baselines, metrics (e.g., success rate, sample efficiency), experimental protocol, or even a high-level description of the decomposition or constraints; this is load-bearing because the contribution is presented as an outcome rather than a derivation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comments. The single major comment concerns the abstract; we address it directly below and agree that a revision is warranted to better support the central claim.

read point-by-point responses

  1. Referee: [—] Abstract: the central empirical claim that 'skill learning becomes efficient and stable with prior knowledge from theory' is asserted without any quantitative results, baselines, metrics (e.g., success rate, sample efficiency), experimental protocol, or even a high-level description of the decomposition or constraints; this is load-bearing because the contribution is presented as an outcome rather than a derivation.

    Authors: The abstract is intended as a concise summary of the full manuscript, which details the decomposition into physics-constrained components, the experimental protocol (including object variations, noise, latency, and friction conditions), baselines, success rates, and sample-efficiency metrics in the Experiments and Results sections. We agree, however, that the abstract itself should more explicitly preview these elements to stand alone. We will revise the abstract to include a high-level description of the decomposition and constraints together with key quantitative outcomes (e.g., success-rate improvements and reduced sample complexity relative to end-to-end RL). revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The abstract and description present an empirical approach: decomposing dexterous skills into simpler components, each learned under constraints from classical physics and control theory, with results framed as experimental outcomes across objects, noise, latency, and friction. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains are visible that would reduce any claimed result to its own inputs by construction. The derivation chain is therefore self-contained against external benchmarks, with the central claim left to validation by unseen experiments rather than deductive necessity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no specific free parameters, axioms, or invented entities; full paper would be needed to populate the ledger.

pith-pipeline@v0.9.1-grok · 5660 in / 938 out tokens · 37969 ms · 2026-06-29T04:11:56.692640+00:00 · methodology

discussion (0)

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Reference graph

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