Hybrid Residual Reinforcement Learning for Contact-Rich Robotic Book Insertion

arXiv 2609.19962 ↗First posted 17 Sep 2026cs.ROcited by 0

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

Abstract (arXiv)

Placing a grasped book into a tight shelf is a compact but difficult contact-rich control problem: millimetre-scale pose error can turn a geometrically valid approach into jamming, failed release, or incomplete seating. We study this final phase after grasp acquisition and global approach, and ask how control authority should be divided between known geometry and learned behaviour. Our method retains a nominal task-space controller for structured insertion and seating, while residual PPO supplies bounded local corrections and decides when to release. Only the brief open-retreat-reclose transition is scripted. For the final policy used on hardware, a deployment-matched simulation evaluation over 512 fixed conditions yields 98.50 percent mean success (0.23 percentage-point sample SD) across three independent training runs, compared with 37.89 percent for nominal control. On the physical xArm7, 60 trials over 30 matched conditions show the same qualitative advantage: residual control raises success from 26.7 percent to 63.3 percent, reduces failures from 22 to 11, and wins 13 of the 15 matched conditions in which the two controllers differ. Robustness tests show that performance remains above 87 percent under initialization perturbations up to 1.5x, while very tight clearances expose the geometric limit of local correction. These results support a hybrid design in which geometry preserves reliable task structure and learning is concentrated on the contact-sensitive behaviour that fixed rules handle poorly.

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 14 of its 19 references that are in the map. First posted on arXiv 17 Sep 2026.

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2024Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning16 Apr

Other references · 13

2018Residual Reinforcement Learning for Robot Controlcited by 274
2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 122
2024From Imitation to Refinement -- Residual RL for Precise Assemblycited by 90
2020Meta-Reinforcement Learning for Robotic Industrial Insertion Taskscited by 34
2021Residual Reinforcement Learning from Demonstrationscited by 31
2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulationcited by 28
2021A Survey of Robot Manipulation in Contactcited by 28
2020Task-driven Perception and Manipulation for Constrained Placement of Unknown Objectscited by 12
2025AnyPlace: Learning Generalized Object Placement for Robot Manipulationcited by 11
2021Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertaintycited by 10
2022Placing by Touching: An empirical study on the importance of tactile sensing for precise object placingcited by 4
2023Reactive Correction of Object Placement Errors for Robotic Arrangement Taskscited by 1

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2024Planning for quasi-static manipulation tasks via an intrinsic haptic metric: a book insertion case studycited by 1

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