mlsanityData to 14 Oct 2026

A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation

arXiv 2510.12477 ↗First posted 14 Oct 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

In a Human-Robot Cooperation (HRC) environment, safety and efficiency are the two core properties to evaluate robot performance. However, safety mechanisms usually hinder task efficiency since human intervention will cause backup motions and goal failures of the robot. Frequent motion replanning will increase the computational load and the chance of failure. In this paper, we present a hybrid Reinforcement Learning (RL) planning framework which is comprised of an interactive motion planner and a RL task planner. The RL task planner attempts to choose statistically safe and efficient task sequences based on the feedback from the motion planner, while the motion planner keeps the task execution process collision-free by detecting human arm motions and deploying new paths when the previous path is not valid anymore. Intuitively, the RL agent will learn to avoid dangerous tasks, while the motion planner ensures that the chosen tasks are safe. The proposed framework is validated on the cobot in both simulation and the real world, we compare the planner with hard-coded task motion planning methods. The results show that our planning framework can 1) react to uncertain human motions at both joint and task levels; 2) reduce the times of repeating failed goal commands; 3) reduce the total number of replanning requests.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 32 references that are in the map. First posted on arXiv 14 Oct 2025.

Builds on

8 of 32 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2020Integrated Task and Motion Planningcited by 290
  4. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 66
  5. 2021Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Taskscited by 5
  6. 2016I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Predictioncited by 4
  7. 2019Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learningcited by 4
  8. 2017Dynamic Path Planning and Replanning for Mobile Robots using RRT*cited by 2

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  1. 2025ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration
  2. 2026A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration
  3. 2025Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning
  4. 2026Turning Safety into Competence: Minimally Exploitable Robot Policies via Safety-Filtered Reinforcement Learning
  5. 2023Safe Reinforcement Learning in a Simulated Robotic Arm
  6. 2024Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
  7. 2025Skill-based Safe Reinforcement Learning with Risk Planning
  8. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review