Perception-Action Coupling Target Tracking Control for a Snake Robot via Reinforcement Learning.

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Vision

Background:

• Visual-guided locomotion in snake-like robots is complex due to intricate body dynamics and the challenge of integrating vision with motor control.
• Coordinating vision and locomotion sub-tasks often requires extensive, trial-and-error tuning, hindering efficient development.

Purpose of the Study:

• To develop a novel, unified approach for visual-guided target tracking in snake-like robots.
• To overcome the limitations of traditional methods by employing a model-free reinforcement learning (RL) algorithm for end-to-end control.

Main Methods:

• Implemented a model-free reinforcement learning (RL) algorithm to directly map visual observations to snake-like robot joint positions.
• Designed a customized reward function to train the RL controller in dynamic and unpredictable tracking scenarios.
• Evaluated the controller's performance across four distinct tracking scenarios.

Main Results:

• The RL-based controller exhibited excellent adaptive locomotion abilities in response to unpredictable target movements.
• The proposed approach demonstrated superior tracking accuracy compared to traditional model-based controllers.
• The end-to-end RL strategy effectively integrated visual perception and complex robotic locomotion.

Conclusions:

• Reinforcement learning offers a powerful and efficient solution for end-to-end visual-guided locomotion and target tracking in snake-like robots.
• The developed RL controller provides robust adaptive capabilities, outperforming conventional methods in dynamic environments.
• This work advances the field of robotic control by enabling seamless integration of vision and complex motion planning.