mlsanityData to 14 Oct 2026

Reinforcement Learning for Robotic Insertion of Flexible Cables in Industrial Settings

arXiv 2509.13731 ↗First posted 17 Sep 2025cs.ROcited by 0

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

Abstract (arXiv)

The industrial insertion of flexible flat cables (FFCs) into receptacles presents a significant challenge owing to the need for submillimeter precision when handling the deformable cables. In manufacturing processes, FFC insertion with robotic manipulators often requires laborious human-guided trajectory generation. While Reinforcement Learning (RL) offers a solution to automate this task without modeling complex properties of FFCs, the nondeterminism caused by the deformability of FFCs requires significant efforts and time on training. Moreover, training directly in a real environment is dangerous as industrial robots move fast and possess no safety measure. We propose an RL algorithm for FFC insertion that leverages a foundation model-based real-to-sim approach to reduce the training time and eliminate the risk of physical damages to robots and surroundings. Training is done entirely in simulation, allowing for random exploration without the risk of physical damages. Sim-to-real transfer is achieved through semantic segmentation masks which leave only those visual features relevant to the insertion tasks such as the geometric and spatial information of the cables and receptacles. To enhance generality, we use a foundation model, Segment Anything Model 2 (SAM2). To eleminate human intervention, we employ a Vision-Language Model (VLM) to automate the initial prompting of SAM2 to find segmentation masks. In the experiments, our method exhibits zero-shot capabilities, which enable direct deployments to real environments without fine-tuning.

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 28 of its 30 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

28 of 30 references.

References · 28

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  5. 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 760
  6. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 694
  7. 2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 585
  8. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 523
  9. 2020Reinforcement Learning with Augmented Datacited by 468
  10. 2022VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Trainingcited by 395
  11. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 372
  12. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 319
16 more
  1. 2018Residual Reinforcement Learning for Robot Controlcited by 274
  2. 2019Improving Sample Efficiency in Model-Free Reinforcement Learning from Imagescited by 274
  3. 2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 122
  4. 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 64
  5. 2023IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Realitycited by 53
  6. 2020Sim2Real Transfer for Reinforcement Learning without Dynamics Randomizationcited by 47
  7. 2022NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fieldscited by 44
  8. 2018A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learningcited by 36
  9. 2020Meta-Reinforcement Learning for Robotic Industrial Insertion Taskscited by 34
  10. 2021Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Studycited by 30
  11. 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulationcited by 28
  12. 2020Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentationcited by 21
  13. 2020Learning Dense Rewards for Contact-Rich Manipulation Taskscited by 19
  14. 2022Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptationcited by 4
  15. 2021Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Taskscited by 2
  16. 2023Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularizationcited by 1

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  1. 2025Memory-updated-based Framework for 100% Reliable Flexible Flat Cables Insertion ↗
  2. 2026SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
  3. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
  4. 2026WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
  5. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learning
  6. 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
  7. 2026Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation ↗
  8. 2024Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues