Inverse Manipulation through Symbolic Planning and Residual Operator Learning

2026-06-03

AI Summary

This work addresses the challenge of reversing robotic manipulation tasks, which cannot be reliably achieved through symbolic state inversion or trajectory replay alone due to the complexities of continuous dynamics that prevent symbolic inverse planning from accurately reproducing forward execution outcomes. To overcome this limitation, the authors propose a novel paradigm that integrates symbolic inverse planning with residual reinforcement learning. Specifically, soft geometric predicates are automatically extracted from demonstrations to construct STRIPS-style operators and define inverse goals. A task planner then invokes primitive actions to coarsely reverse the task, followed by fine-tuning via a Soft Actor-Critic algorithm that learns a residual policy to precisely satisfy any unmet symbolic predicates. Evaluated on the ManiSkill3 PushCube task, the approach successfully achieves full pose reversal of the cube while maintaining both symbolic logical consistency and physical feasibility.

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.

Problem

• inverse manipulation
• symbolic planning
• residual operator learning
• robotic task
• continuous interaction dynamics

Innovation

• inverse manipulation
• symbolic planning
• residual operator learning
• soft geometric predicates
• reinforcement learning