mlsanityData to 14 Oct 2026

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks

arXiv 2509.13949 ↗First posted 17 Sep 2025cs.ROcited by 3

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

Abstract (arXiv)

High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we present SHaRe-RL, a reinforcement learning framework that leverages multiple sources of prior knowledge. By (i) structuring skills into manipulation primitives, (ii) incorporating human demonstrations and online corrections, and (iii) bounding interaction forces with per-axis compliance, SHaRe-RL enables efficient and safe online learning for long-horizon, contact-rich industrial assembly tasks. Experiments on the insertion of industrial Harting connector modules with 0.2-0.4 mm clearance demonstrate that SHaRe-RL achieves reliable performance within practical time budgets. Our results show that process expertise, without requiring robotics or RL knowledge, can meaningfully contribute to learning, enabling safer, more robust, and more economically viable deployment of RL for industrial assembly.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 32 of its 51 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

32 of 51 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks27 Aug
  2. 2024Precision-Focused Reinforcement Learning Model for Robotic Object Pushing13 Nov
  3. 2024Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning29 Oct
  4. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning29 Jan

Other references · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  5. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  6. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  7. 2022VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Trainingcited by 395
  8. 2023Efficient Online Reinforcement Learning with Offline Datacited by 363
  9. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 319
  10. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 276
  11. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
  12. 2018Safe Exploration in Continuous Action Spacescited by 247
16 more
  1. 2020Generalization in Reinforcement Learning by Soft Data Augmentationcited by 137
  2. 2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 122
  3. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 95
  4. 2024From Imitation to Refinement -- Residual RL for Precise Assemblycited by 90
  5. 2019Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Taskscited by 67
  6. 2021Accelerating Robotic Reinforcement Learning via Parameterized Action Primitivescited by 65
  7. 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 64
  8. 2023Imitation Bootstrapped Reinforcement Learningcited by 64
  9. 2015Reinforcement Learning with Parameterized Actionscited by 63
  10. 2019COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Explorationcited by 57
  11. 2020Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approachcited by 29
  12. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Visioncited by 13
  13. 2015A Framework for Fine Robotic Assemblycited by 8
  14. 2023ConBaT: Control Barrier Transformer for Safe Policy Learningcited by 3
  15. 2025End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation Systemcited by 2
  16. 2023Reinforcement Learning with Parameterized Manipulation Primitives for Robotic Assemblycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025ReEXplore: Improving MLLMs for Embodied Exploration with Contextualized Retrospective Experience Replaycited by 4
  2. 2025MagBotSim: Physics-Based Simulation and Reinforcement Learning Environments for Magnetic Roboticscited by 0
  3. 2026Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework
  2. 2024ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly
  3. 2026Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control ↗
  4. 2019Contact Skill Imitation Learning for Robot-Independent Assembly Programming
  5. 2025RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning
  6. 2026VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation
  7. 2026Learning-Based Strategy for Composite Robot Assembly Skill Adaptation ↗
  8. 2025RM-RL: Role-Model Reinforcement Learning for Precise Robot Manipulation