mlsanityData to 14 Oct 2026

ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration

arXiv 2512.03707 ↗First posted 3 Dec 2025cs.ROcited by 0

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

Abstract (arXiv)

In collaborative human-robot tasks, safety requires not only avoiding collisions but also ensuring safe, intentional physical contact. We present ContactRL, a reinforcement learning (RL) based framework that directly incorporates contact safety into the reward function through force feedback. This enables a robot to learn adaptive motion profiles that minimize human-robot contact forces while maintaining task efficiency. In simulation, ContactRL achieves a low safety violation rate of 0.2% with a high task success rate of 87.7%, outperforming state-of-the-art constrained RL baselines. In order to guarantee deployment safety, we augment the learned policy with a kinetic energy based Control Barrier Function (eCBF) shield. Real-world experiments on an UR3e robotic platform performing small object handovers from a human hand across 360 trials confirm safe contact, with measured normal forces consistently below 10N. These results demonstrate that ContactRL enables safe and efficient physical collaboration, thereby advancing the deployment of collaborative robots in contact-rich tasks.

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 13 of its 31 references that are in the map. First posted on arXiv 3 Dec 2025.

Builds on

13 of 31 references.

References · 13

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  3. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 70
  4. 2019Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Taskscited by 67
  5. 2021Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violationscited by 39
  6. 2023Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functionscited by 13
  7. 2022Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functionscited by 11
  8. 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 10
  9. 2023Force-Constrained Visual Policy: Safe Robot-Assisted Dressing via Multi-Modal Sensingcited by 10
  10. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 8
  11. 2022ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interactioncited by 7
  12. 2023PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learningcited by 7
1 more
  1. 2022Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Taskscited by 2

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  1. 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
  2. 2024SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks
  3. 2025Contact-Safe Reinforcement Learning with ProMP Reparameterization and Energy Awareness
  4. 2026Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
  5. 2025Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
  6. 2025Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks
  7. 2025Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
  8. 2025Reinforcement Learning for Robotic Safe Control with Force Sensing