Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916632031395840 |
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| author | Singh, Siddharth Yu, Tian Chang, Qing Karigiannis, John Liu, Shaopeng |
| author_facet | Singh, Siddharth Yu, Tian Chang, Qing Karigiannis, John Liu, Shaopeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing Singh, Siddharth Yu, Tian Chang, Qing Karigiannis, John Liu, Shaopeng Robotics 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. |
| title | Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.19340 |