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Related Experiment Video

Updated: Feb 8, 2026

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

Adaptive Obstacle Avoidance for Robotic Arms Using Hierarchical Reinforcement Learning and Path Optimization.

Chun Ma¹, Tiecheng Su¹, Yuede Pan¹

¹College of Mechanical Engineering, Guangxi University, Nanning, China.

Annals of the New York Academy of Sciences

|February 6, 2026

Summary

This summary is machine-generated.

This study introduces a new robotic arm motion planning method that combines Proximal Policy Optimization (PPO) and Rapidly-exploring Random Tree star (RRT*) for dynamic obstacle avoidance, achieving an 87.6% success rate.

Keywords:

curriculum learning (CL)dynamic obstacle avoidance motion planning proximal policy optimization (PPO)rapidly exploring random tree star (RRT*)

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Area of Science:

Robotics
Artificial Intelligence
Motion Planning

Background:

Dynamic obstacle avoidance is a significant challenge in robotic arm motion planning.
Traditional methods struggle with adaptive decision-making and precise trajectory generation in unstructured environments.

Purpose of the Study:

To present a novel hierarchical motion planning framework for dynamic obstacle avoidance.
To enhance the capabilities of robotic arms in complex, unstructured environments.

Main Methods:

A hierarchical framework combining Proximal Policy Optimization (PPO) and Rapidly-exploring Random Tree star (RRT*).
Curriculum learning paradigm for training PPO on progressively difficult scenarios.
A multiobjective reward function balancing exploration and exploitation using step-efficiency and artificial potential field principles.

Main Results:

Achieved an 87.6% success rate in dynamic obstacle scenarios.
Outperformed standalone PPO and existing hybrid reinforcement learning approaches.
Demonstrated effective global obstacle avoidance strategies (PPO) and refined local trajectories (RRT*).

Conclusions:

The proposed framework offers a practical solution for dynamic obstacle avoidance in robotic arms.
The method shows broader applicability to high-dimensional autonomous manipulation tasks.
The hierarchical approach effectively balances global strategy learning with fine motor control.