mlsanityData to 14 Oct 2026

Reinforcement Learning for Robotic Safe Control with Force Sensing

arXiv 2512.02022 ↗First posted 30 Oct 2025cs.ROcited by 0

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

Abstract (arXiv)

For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.

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 36 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

14 of 36 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2017Hindsight Experience Replaycited by 1,136
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  5. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  6. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  7. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  8. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  9. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  10. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  11. 2014Safe Exploration of State and Action Spaces in Reinforcement Learningcited by 38
  12. 2016Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstrationcited by 36
2 more
  1. 2018Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical systemcited by 17
  2. 2017Autonomous Quadrotor Landing using Deep Reinforcement Learningcited by 4

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  1. 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
  2. 2025Towards Forceful Robotic Foundation Models: a Literature Survey
  3. 2025The Role of Tactile Sensing for Learning Reach and Grasp
  4. 2024Sim2Real Manipulation on Unknown Objects with Tactile-based Reinforcement Learning
  5. 2026PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation
  6. 2025FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation
  7. 2025ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration
  8. 2024Learning Tactile Insertion in the Real World