Neural Gait Synthesis for Autonomous Biped Robots

该奖项为两足行走机器人的反射步态合成和适应困难地形的综合方法的初步研究提供资金。该研究将探索基于神经网络的机制，用于训练中央模式生成器以生成新的步态，该步态由地形监测学习和适应单元学习。它还将研究用于学习和适应单元的反向传播和强化学习机制的收敛特性。这项双足机器人研究的长期目标是实现比多足行走机器或其他形式的机器运动更敏捷和机动性，并最终提供移动机器人在危险操作或崎岖地形中增强或取代人类的能力。

This award funds initial research on integration of methods for reflexive gait synthesis and adaptation to difficult terrains, for a biped walking robot. The research will explore neural- network-based mechanisms for training a central pattern generator to generate a new gait that is learned by a terrain-monitoring learning and adaptation unit. It will also study the convergence properties of backpropagation and reinforcement learning mechanisms for use in the learning and adaptation unit. The long-term goal of this research in biped robotics is to achieve more agility and mobility than is possible in multilegged walking machines or other forms of machine locomotion, and ultimately to provide capability for mobile robots to augment or replace humans in hazardous operations or in rough terrain.