Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing

TL;DR

This paper presents a hybrid robot motion planning approach combining task space RL-LfD and joint space DRL agents, with a higher-level agent switching between them to generate feasible, smooth trajectories in manufacturing environments.

Contribution

A novel multi-level hybrid motion planning method integrating RL-LfD and DRL with a switching mechanism for improved feasibility and smoothness in robot trajectories.

Findings

Effective in simulated robotic scenarios

Validated in real-world manufacturing setup

Generates feasible, smooth trajectories respecting constraints

Abstract

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.