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Stable Jumping Control Based on Deep Reinforcement Learning for a Locust-Inspired Robot.

Qijie Zhou1,2, Gangyang Li1,2, Rui Tang1,2

  • 1Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Biomimetics (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a stable jumping control algorithm for locust-inspired robots using deep reinforcement learning. The novel method enhances jumping accuracy and stability, enabling energy-efficient locomotion.

Keywords:
biologically inspired robotsdeep reinforcement learningdynamic stability

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Biomechanical Engineering

Background:

  • Biologically inspired jumping robots offer advanced mobility but struggle with posture stability during jumps.
  • Rapid postural changes significantly degrade the accuracy and stability of robotic jumping movements.

Purpose of the Study:

  • To develop a stable jumping control algorithm for a locust-inspired robot using deep reinforcement learning.
  • To enhance the stability and accuracy of jumping movements in legged robots.

Main Methods:

  • A deep reinforcement learning framework with actor and critic networks was employed.
  • The algorithm maps robot observations directly to joint torques, incorporating an entropy term for exploration.
  • A dynamic reward function with a stage incentive mechanism was designed to improve stability and accuracy.

Main Results:

  • The locust-inspired robot demonstrated smooth, non-flip jumps with less than 3% error in target distance accuracy.
  • Jumping locomotion consumed 44.6% less energy compared to walking for the same distance.
  • The proposed algorithm showed faster convergence and better performance than classical methods.

Conclusions:

  • Deep reinforcement learning provides an effective approach for stable and accurate robotic jumping.
  • The developed algorithm significantly improves energy efficiency and locomotion capabilities in jumping robots.
  • This research paves the way for more agile and efficient bio-inspired robots capable of complex terrain traversal.