MPC for humanoid controlDownload PDF

Jun 18, 2020 (edited Jul 02, 2020)RSS 2020 Workshop RobRetro SubmissionReaders: Everyone
  • Keywords: model predictive control, legged locomotion
  • TL;DR: We developed a Model Predictive Control (MPC) approach that takes full-body dynamics into account to generate human-like behaviors, especially in terms of versatility and agility.
  • Abstract: Future humanoid robots are expected to work in real environments such as extremely hazardous situations instead of humans. However, they still have difficulty in generating human-like behaviors, especially in terms of versatility and agility. To cope with this problem, we developed a Model Predictive Control (MPC) approach that takes full-body dynamics into account. MPC with full-body dynamics is a good candidate for generating a wide variety of agile movements because 1. We do not have to design the low-level details of each movement. Such labor-intensive tasks are automated through numerical optimization. 2. Unlike using a highly reduced model of a humanoid robot, the optimization process under the constraints of full-body dynamics does not restrict the generable agile movements. A well-known control approach is a hierarchical control architecture using an inverted pendulum and inverse dynamics control. However, an optimization process under such reduced model restricts the generable agile movements since the inverted pendulum model cannot take detailed limb movements into account. We evaluated our approach in skating tasks with simulated and real lower-body humanoids that have rollers on the feet. We are interested in generating wide variety of human-like agile motions. Then, we selected the skating movement generation as an illustrative task. Our simulated robot generated various agile motions such as flipping down from a cliff, and our real lower-body humanoid also successfully generated a movement down a slope.
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