仿人机器人行走稳定性研究是机器人领域一大研究热点,目前主要依据动力学模型规划稳定步态,但依靠步态规划形成的运动模式往往需要复杂的运算,并且机器人的运动形式单一.为实现机器人多样化步态的生成,在模仿学习的框架下对机器人的步态模仿问题展开研究,利用人体行走信息作为示教数据,实现仿人机器人对人体行走过程的模仿学习,在简化运动规划的同时使机器人的运动步态更具多样化与拟人化.为满足机器人在步态模仿过程中的稳定性,基于零力矩点(zero moment point,ZMP)判据补偿质心偏移,利用滞回曲线确定行走过程中支撑脚的切换以实现稳定性控制.基于NAO机器人的模仿学习系统仿真研究结果表明:ZMP判据的引入有效地保证了机器人对人体示教步态模仿的稳定性,基于滞回曲线的支撑脚选取保证了支撑脚切换的平稳.
Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learning automaton called a Skinner au- tomaton that is a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method as well as Metropolis algo- rithm and simulated annealing. Under certain conditions, we prove that the Skinner automaton is expedient, 6-optimal, optimal, and that the operant probabilities converge to the set of stable roots with probability of 1. The Skinner automaton enables ma- chines to autonomously learn in an animal-like way.
