Robotic Manipulator Motion Planning and Obstacle Avoidance Using Physics-Informed Reinforcement Learning via Artificial Potential Fields

Traditional planning algorithms often struggle with high-dimensional spaces or local minima traps, while stan
dard Deep Reinforcement Learning (DRL) approaches suffer from sparse rewards and lack physical safety
guarantees. To bridge this gap, this paper proposes a Physics-Informed Reinforcement Learning (PIRL)
framework that integrates Artificial Potential Fields (APF) with Proximal Policy Optimization (PPO) for the
continuous control of an n-Degree-of-Freedom (DoF) robotic manipulator. Rather than using APF as a stan
dalone deterministic controller, the proposed approach leverages the APF gradient as a dense reward-shaping
mechanism. This hybrid architecture guides the agent toward its target while strictly avoiding obstacles, ef
fectively overcoming the sparse reward problem and mitigating classical local minima. The proposed method
is validated by a comprehensive 17-task benchmark suite of varying complexities in a simulated environment.
Experimental results with a 6-DoF manipulator show that the APF-PPO agent reaches the target under deter
ministic evaluation in all 17 tasks, against 4 for a classical APF controller operating on the same potential field,
generating collision-free trajectories and escaping the configurations in which that controller is trapped by lo
cal minima. A demo video is provided along with this paper (Available at: https://youtu.be/XZNDLkh75Go).