Abstract
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.
Highlights
A LTHOUGH humanoid robots have been promoted for working in such real environments as hazardous situations instead of humans, they remain difficult to generate human-like behaviors, especially in terms of versatility and agility [1].We can partially attribute their lack of versatility and agility to their control approaches that approximate a humanoid robot as a highly reduced model like an inverted pendulum [2], [3]
An online optimal control approach known as model predictive control (MPC) generated simulated robot motions under full-body dynamics [4]
We proposed an optimization process by deriving fast dynamics of a humanoid robot, which is used in the lower-layer MPC in our hierarchical approach
Summary
A LTHOUGH humanoid robots have been promoted for working in such real environments as hazardous situations instead of humans, they remain difficult to generate human-like behaviors, especially in terms of versatility and agility [1]. We can partially attribute their lack of versatility and agility to their control approaches that approximate a humanoid robot as a highly reduced model like an inverted pendulum [2], [3]. Such a model can only be applied to a limited range of tasks since generable movements are significantly restricted [4]. A real humanoid robot has to be controlled for a long control period due to large computational time, and MPC has been applied to limited tasks in which fast movements are unnecessary [7] To cope with this problem, we propose a novel multi-timescale control framework in this letter. Our real humanoid successfully generated a movement for going down a slope as an initial attempt to show control performance of our proposed method in the real environment towards the agile skating motion generation
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