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arxiv: 2606.05248 · v1 · pith:6RMHF6YVnew · submitted 2026-06-03 · 💻 cs.RO

Inverse Manipulation through Symbolic Planning and Residual Operator Learning

Yigit Yildirim , Giuseppe Rauso , Riccardo Caccavale , Alberto Finzi This is my paper

Pith reviewed 2026-06-28 06:16 UTC · model grok-4.3

classification 💻 cs.RO
keywords inverse manipulationsymbolic planningresidual learningreinforcement learningSTRIPS operatorsrobotic manipulationpredicate-based control
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The pith

Predicate-derived residual reinforcement learning turns approximate symbolic inverse plans into accurate robotic manipulation inverses.

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

The paper develops a hybrid method that extracts STRIPS-like operators from robot demonstrations using soft geometric predicates and then builds inverse restoration objectives for each operator. These objectives aim to preserve preconditions, restore deleted effects, and negate added effects from the forward task. A symbolic planner first tries to meet the objectives with available primitives, after which any remaining unsatisfied predicates are turned into a residual learning problem solved by reinforcement learning. The approach is tested on a pushing task where the symbolic part handles coarse restoration and the learned residual policy refines the final pose. A reader would care if this shows a practical way to make symbolic inverses work under real continuous dynamics without discarding the high-level plan.

Core claim

For each extracted operator an inverse restoration objective is defined that preserves preconditions, restores delete effects, and negates add effects. A task planner attempts to satisfy the objective with action primitives; unresolved predicates then induce a residual operator learning problem solved through reinforcement learning. On the ManiSkill3 PushCube task the symbolic inverse produces a coarse pick-and-place restoration while a Soft Actor-Critic policy refines the cube pose to meet the remaining predicates, demonstrating that predicate-derived residual control can convert an approximate symbolic inverse into a physically grounded inverse skill.

What carries the argument

The residual operator learning problem induced by unresolved symbolic predicates after symbolic planning, solved via reinforcement learning to refine the inverse skill.

If this is right

  • Symbolic planning can produce coarse but structurally valid inverse plans that residual policies then complete.
  • Residual reinforcement learning can be scoped to specific unsatisfied predicates rather than the full task.
  • The same extracted operators support both forward execution and inverse restoration without modification.
  • Hybrid symbolic-plus-residual methods can address continuous interaction dynamics that pure symbolic inversion cannot handle.

Where Pith is reading between the lines

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

  • The method may generalize to tasks where symbolic abstractions exist but exact inverse dynamics are unknown.
  • If residual policies remain local to each operator, the approach could support longer-horizon inverse planning without exponential growth in learning complexity.
  • A natural extension would be to measure how much the residual component reduces the number of symbolic planning failures across varied initial states.

Load-bearing premise

Reinforcement learning can resolve any remaining predicates without invalidating the overall symbolic plan or requiring changes to the extracted operators.

What would settle it

A test run in which the trained residual policy consistently fails to satisfy the unresolved inverse predicates on held-out executions of the pushing task.

Figures

Figures reproduced from arXiv: 2606.05248 by Alberto Finzi, Giuseppe Rauso, Riccardo Caccavale, Yigit Yildirim.

Figure 1
Figure 1. Figure 1: The overview of the proposed system. Skill [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The reward for the residual RL. The contribution [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Screenshot from a single evaluation: (a) the cube [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Inverting a robotic task requires more than reversing symbolic state transitions or rewinding motor trajectories. In robot manipulation tasks, symbolic inverse plans often fail to fully restore the effects of forward executions under continuous interaction dynamics. We present a hybrid framework for inverse manipulation that derives inverse-skill objectives from STRIPS-like operators automatically extracted from demonstrations through soft geometric predicates. For each extracted operator, we construct an inverse restoration objective that preserves preconditions, restores delete effects, and negates add effects. A task planner first attempts to satisfy this objective using available action primitives. Unresolved symbolic predicates then induce a residual operator learning problem solved through Reinforcement Learning (RL). We evaluate the framework on the ManiSkill3 PushCube task. For a forward pushing skill, the symbolic inverse performs a coarse pick-and-place restoration, while a residual Soft Actor-Critic policy refines the cube pose to satisfy the remaining inverse predicates. Our results show that predicate-derived residual control can turn an approximate symbolic inverse into a physically grounded inverse skill.

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 / 1 minor

Summary. The manuscript presents a hybrid framework for inverse robotic manipulation. It automatically extracts STRIPS-like operators from demonstrations via soft geometric predicates, derives inverse restoration objectives that preserve preconditions and negate add/delete effects, applies a task planner to attempt symbolic inversion, and formulates unresolved predicates as a residual operator learning problem solved by RL (Soft Actor-Critic). On the ManiSkill3 PushCube task, the symbolic inverse yields a coarse pick-and-place restoration while the residual policy refines cube pose to satisfy remaining inverse predicates, demonstrating that predicate-derived residual control can produce a physically grounded inverse skill.

Significance. If the central claim holds, the framework provides a structured method to derive inverse skills from forward demonstrations by using symbolic predicates to define a residual learning objective, potentially improving sample efficiency and interpretability over pure RL or pure symbolic approaches. The automatic extraction of operators and the inverse restoration objective are strengths that ground the learning problem in the symbolic plan without requiring manual redesign of operators.

major comments (1)
  1. [Evaluation] Evaluation section: the results on ManiSkill3 PushCube are described only at a high level (coarse pick-and-place plus residual refinement) with no reported quantitative metrics such as success rate, final pose error, predicate satisfaction rate, or comparisons to baselines (pure symbolic planner, pure SAC, or alternative residual formulations). This absence is load-bearing for the claim that 'predicate-derived residual control can turn an approximate symbolic inverse into a physically grounded inverse skill.'
minor comments (1)
  1. [Abstract] Abstract: the description of the inverse restoration objective could explicitly reference the three components (preserve preconditions, restore delete effects, negate add effects) to improve clarity for readers unfamiliar with the framework.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We agree that the evaluation requires quantitative metrics and baseline comparisons to substantiate the central claims, and we will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the results on ManiSkill3 PushCube are described only at a high level (coarse pick-and-place plus residual refinement) with no reported quantitative metrics such as success rate, final pose error, predicate satisfaction rate, or comparisons to baselines (pure symbolic planner, pure SAC, or alternative residual formulations). This absence is load-bearing for the claim that 'predicate-derived residual control can turn an approximate symbolic inverse into a physically grounded inverse skill.'

    Authors: We acknowledge that the evaluation in the current manuscript is presented at a high level without quantitative metrics or baseline comparisons. This is a valid concern that weakens support for the claim. In the revised version, we will expand the evaluation section with new experiments reporting success rates, final pose errors, predicate satisfaction rates, and comparisons to baselines including pure symbolic planning, pure SAC, and alternative residual formulations. These results will be added to demonstrate the effectiveness of the predicate-derived residual control. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes a hybrid symbolic-RL framework for inverse manipulation: STRIPS-like operators are extracted from demonstrations via soft predicates, inverse restoration objectives are constructed from those operators' add/delete effects, a planner is applied, and RL resolves remaining predicates. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text that would make any claimed result equivalent to its inputs by construction. The central claim (predicate-derived residual control produces a grounded inverse skill) is supported by the described mechanism and task evaluation rather than reducing to a definitional loop or prior self-work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are detailed.

pith-pipeline@v0.9.1-grok · 5705 in / 985 out tokens · 32651 ms · 2026-06-28T06:16:58.409717+00:00 · methodology

discussion (0)

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Reference graph

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