Planning of Goal-oriented Motion from Stochastic Motion Primitives and Optimal Controlling of Joint Torques in Whole-body
Planning of Goal-oriented Motion from Stochastic Motion Primitives and Optimal Controlling of Joint Torques in Whole-body
复制标题
基于随机运动原语的目标导向运动规划和全身关节扭矩的优化控制
DOI：
10.1016/j.robot.2017.01.013
复制
发表时间：
2017
影响因子：
4.3
通讯作者：
Yoshihiko Nakamura
中科院分区：
文献类型：
--
作者：
Wataru Takano;Yoshihiko Nakamura
人形机器人有望融入日常生活。这要求机器人能够执行人类容易理解的类似人类的动作。通过模仿学习是一个有效的框架，使机器人能够产生与人类相同的动作。然而，对于机器人来说，生成与学习的运动完全相同的运动通常是没有用的，因为环境可能与学习运动的环境不同。人形机器人应该通过修改学习的动作来合成适应当前环境的动作。先前的研究将捕获的人体全身运动编码为隐马尔可夫模型（以下称为运动原语），并根据获取的运动原语生成类人运动。身体与环境的接触也需要受到控制，以便仿人机器人在其当前环境下实现全身运动。本文提出了一种使用运动原语合成运动学数据并控制人形机器人中所有关节扭矩的新方法，以实现所需的全身运动和接触力。实验证明了所提出的仿人机器人合成和控制全身运动方法的有效性。
Humanoid robots are expected to be integrated into daily life. This requires the robots to perform human-like actions that are easily understandable by humans. Learning by imitation is an effective framework that enables the robots to generate the same motions that humans do. However, it is generally not useful for the robots to generate motions that are precisely the same as learned motions because the environment is likely to be different from the environment where the motions were learned. The humanoid robot should synthesize motions that are adaptive to the current environment by modifying learned motions. Previous research encoded captured human whole-body motions into hidden Markov models, which are hereafter referred to as motion primitives, and generated human-like motions based on the acquired motion primitives. The contact between the body and the environment also needs to be controlled, so that the humanoid robot’s whole-body motion can be realized in its current environment. This paper proposes a novel approach to synthesizing kinematic data using the motion primitive and controlling the torques of all the joints in the humanoid robot to achieve the desired whole-body motions and contact forces. The experiments demonstrate the validity of the proposed approach to synthesizing and controlling whole-body motions by humanoid robots.