Learning to acquire whole-body humanoid CoM movements to achieve dynamic tasks
Learning to acquire whole-body humanoid CoM movements to achieve dynamic tasks
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DOI：
10.1109/robot.2007.363871
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发表时间：
2007-04
期刊：
影响因子：
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通讯作者：
Takamitsu Matsubara;J. Morimoto;J. Nakanishi;S. Hyon;Joshua G. Hale;G. Cheng
中科院分区：
文献类型：
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作者：
Takamitsu Matsubara;J. Morimoto;J. Nakanishi;S. Hyon;Joshua G. Hale;G. Cheng
本文提出了一种新的方法来获得动态的全身运动的人形机器人集中学习的控制策略的质量中心。使用策略梯度方法来获取CoM运动作为用于实现期望的动态任务的控制策略。然后使用基于CoM雅可比矩阵的冗余分辨率来计算所有关节的角速度，以实现与通过学习获得的CoM运动一致的全身运动。为了证明我们的方法的有效性，我们将其应用在模拟学习的富士通人形机器人，Hoap-2的强大的冲压运动。
This paper presents a novel approach to acquire dynamic whole-body movements on humanoid robots focused on learning a control policy for the center of mass. A policy-gradient method is used to acquire a CoM movement as a control policy for achieving a desired dynamic task. A CoM-Jacobian-based redundancy resolution is then used to compute angular velocities for all joints in order to achieve a whole-body movement consistent with the CoM movement acquired through learning. To demonstrate the effectiveness of our method, we apply it in simulation to the learning of a strong punching movement on the Fujitsu humanoid robot, Hoap-2.