mlsanityData to 14 Oct 2026

Inverse Manipulation through Symbolic Planning and Residual Operator Learning

arXiv 2606.05248 ↗First posted 3 Jun 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 22 references that are in the map. First posted on arXiv 3 Jun 2026.

Builds on

6 of 22 references.

References · 6

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 191
  3. 2017Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learningcited by 90
  4. 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 58
  5. 2018Forward-Backward Reinforcement Learningcited by 19
  6. 2021There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learningcited by 18

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  1. 2026Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
  2. 2021Residual Reinforcement Learning from Demonstrations
  3. 2026Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation
  4. 2026Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations ↗
  5. 2026Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations ↗
  6. 2020Model-Based Inverse Reinforcement Learning from Visual Demonstrations
  7. 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
  8. 2026Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation