Основні поняття
A principled approach to automatically synthesize reduced-order models that are optimal for a given distribution of locomotion tasks, enabling high-performance motion planning and control.
Анотація
The paper proposes a model optimization algorithm to automatically synthesize reduced-order models (ROMs) that are optimal for a given distribution of locomotion tasks. The key insights are:
The authors define a reduced-order model as a combination of an embedding function r and a dynamics function g, and parameterize these functions using basis functions.
They formulate a bilevel optimization problem to find the optimal parameters of the ROM, where the inner optimization solves a trajectory optimization problem to evaluate the cost of a given ROM, and the outer optimization updates the ROM parameters to minimize the expected cost over the task distribution.
The authors leverage the Envelope Theorem to efficiently compute the gradient of the outer optimization, avoiding the need for computationally intensive implicit differentiation.
They demonstrate the approach on a bipedal robot Cassie, showing that the optimized ROMs can reduce joint torque costs by up to 23% and increase walking speed by up to 54% compared to the initial ROM (a linear inverted pendulum model).
The authors also implement a model predictive control framework using the optimized ROMs, and validate the performance improvements in both simulation and hardware experiments.
Overall, the paper presents a principled approach to discover task-optimal reduced-order models, going beyond manually designed extensions, and demonstrates significant performance gains on a complex legged robot.
Статистика
The optimal reduced-order model reduces the cost of Cassie's joint torques by up to 23% and increases its walking speed by up to 54% compared to the initial linear inverted pendulum model.
Цитати
"We show in simulation that the optimal ROM reduces the cost of Cassie's joint torques by up to 23% and increases its walking speed by up to 54%."
"We also show hardware result that the real robot walks on flat ground with 10% lower torque cost."