# Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing

arXiv: https://arxiv.org/abs/2502.19340 · HTML: https://mlsanity.com/paper/2502.19340/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 8 of 8)

- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments](https://mlsanity.com/paper/2010.11940/) (2020)
- [Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks](https://mlsanity.com/paper/2110.12618/) (2021)
- [Reuleaux: Robot Base Placement by Reachability Analysis](https://mlsanity.com/paper/1710.01328/) (2017)
- [Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations](https://mlsanity.com/paper/1910.07294/) (2019)
- [Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation](https://mlsanity.com/paper/2102.11051/) (2021)
- [An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet](https://mlsanity.com/paper/2105.05985/) (2021)
- [Robotic Arm Control and Task Training through Deep Reinforcement Learning](https://mlsanity.com/paper/2005.02632/) (2020)

## 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.