Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
Industrial robots are widely used in diverse manufacturing environments. Nonetheless, how to enable robots to automatically plan trajectories for changing tasks presents a considerable challenge. Further complexities arise when robots operate within work cells alongside machines, humans, or other robots. This paper introduces a multi-level hybrid robot motion planning method combining a task space Reinforcement Learning-based Learning from Demonstration (RL-LfD) agent and a joint-space based Deep Reinforcement Learning (DRL) based agent. A higher level agent learns to switch between the two agents to enable feasible and smooth motion. The feasibility is computed by incorporating reachability, joint limits, manipulability, and collision risks of the robot in the given environment. Therefore, the derived hybrid motion planning policy generates a feasible trajectory that adheres to task constraints. The effectiveness of the method is validated through sim ulated robotic scenarios and in a real-world setup.
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 8 of its 25 references that are in the map. First posted on arXiv 26 Feb 2025.
Builds on
8 of 25 references.
References · 8
- 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 39
- 2020Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environmentscited by 23
- 2021Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Taskscited by 20
- 2017Reuleaux: Robot Base Placement by Reachability Analysiscited by 11
- 2019Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrationscited by 11
- 2021Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivationcited by 10
- 2021An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybulletcited by 7
- 2020Robotic Arm Control and Task Training through Deep Reinforcement Learningcited by 6
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- 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
- 2024Digital Twin-Driven Reinforcement Learning for Obstacle Avoidance in Robot Manipulators: A Self-Improving Online Training Framework
- 2025RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning
- 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
- 2026Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
- 2024Physical Simulation for Multi-agent Multi-machine Tending
- 2024Learning Manipulation Tasks in Dynamic and Shared 3D Spaces
- 2024Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives