Full-Body Optimal Control Toward Versatile and Agile Behaviors in a Humanoid Robot
Full-Body Optimal Control Toward Versatile and Agile Behaviors in a Humanoid Robot
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DOI：
10.1109/lra.2019.2947001
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发表时间：
2020-01
影响因子：
5.2
通讯作者：
K. Ishihara;Takeshi D. Itoh;J. Morimoto
中科院分区：
文献类型：
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作者：
K. Ishihara;Takeshi D. Itoh;J. Morimoto
在这封信中，我们开发了一个最优控制框架，将人形机器人的全身动力学考虑在内。利用全身动力学，特别是一种被称为模型预测控制（MPC）的在线最优控制方法进行了探索。然而，由于MPC的巨大计算负担，全身运动不能在短时间内更新。因此，MPC通常在有限的任务范围内用物理类人机器人进行评估，其中高速运动执行是不必要的。为了解决这个问题，我们的多时间尺度控制框架通过计算效率高的分层MPC驱动全身运动。与此同时，一个受生物启发的控制器在很短的控制时间内保持机器人的姿势。我们用模拟的和真实的下半身人形机器人来评估我们的框架，这些机器人的脚上有滚轴。我们的模拟机器人产生了各种灵活的动作，比如跳过一个凸起，从悬崖上翻下来。我们真正的下半身人形机器人也成功地产生了一个下坡的运动。
In this letter, we develop an optimal control framework that takes the full-body dynamics of a humanoid robot into account. Employing full-body dynamics has been explored in, especially, an online optimal control approach known as model predictive control (MPC). However, whole-body motions cannot be updated in a short period of time due to MPC's large computational burden. Thus, MPC has generally been evaluated with a physical humanoid robot in a limited range of tasks where high-speed motion executions are unnecessary. To cope with this problem, our multi-timescale control framework drives whole-body motions with a computationally efficient hierarchical MPC. Meanwhile, a biologically inspired controller maintains the robot's posture for a very short control period. We evaluated our framework in skating tasks with simulated and real lower-body humanoids that have rollers on the feet. Our simulated robot generated various agile motions such as jumping over a bump and flipping down from a cliff in real time. Our real lower-body humanoid also successfully generated a movement down a slope.