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Yilin Zhang¹, Huimin Sun¹, Honglin Sun¹

¹Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan.

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|June 26, 2024

Summary

This summary is machine-generated.

This study introduces an adaptive framework for bipedal robots to maintain stability against wind disturbances. The novel approach enhances training speed and walking distance for robots in complex outdoor environments.

Keywords:

adaptive exploration biomimetics exploration biped robot reinforcement learning wind disturbance

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Bipedal robots offer advanced mobility but face stability challenges, especially in outdoor environments with wind.
Existing control methods struggle with the complexity of maintaining balance under dynamic wind conditions.
Enhanced stability is crucial for bipedal robot safety and operational efficiency in real-world applications.

Purpose of the Study:

To develop an adaptive, bio-inspired exploration framework for bipedal robots to overcome wind disturbances.
To improve the ability of bipedal robots to maintain balance and stability in dynamic, unpredictable environments.
To enhance the training efficiency and performance of bipedal robots using advanced reinforcement learning techniques.

Main Methods:

An adaptive bio-inspired exploration framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm was implemented.
The framework enables robots to perceive wind forces and adapt their exploration strategies.
Hindsight Experience Replay (HER) and reward-reshaping were incorporated to address sparse reward challenges and improve training.

Main Results:

Robots utilizing the proposed framework demonstrated faster exploration of stable behaviors under complex conditions.
Significant improvements in training speed and walking distance were observed compared to traditional DDPG algorithms.
The adaptive approach proved effective in enhancing bipedal robot stability against wind disturbances.

Conclusions:

The developed adaptive framework successfully enhances bipedal robot stability and performance in the presence of wind.
The integration of DDPG, HER, and reward-reshaping offers a robust solution for training robots in challenging environments.
This research paves the way for more reliable and efficient deployment of bipedal robots in outdoor applications.