Article

Distance-Field-Based Reinforcement Learning for Reactive and Robust Obstacle Avoidance in Robotic Arms

This paper proposes a reinforcement learning framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues to achieve stable and responsive obstacle avoidance behavior and simplifying reward design; it effectively avoids obstacles both with and without artificial potential field guidance.

Part of the event: Proposes an obstacle avoidance framework for robotic arms that integrates EDF into observation space

This page provides a summary and source link only.