Disney Enterprises, Inc.

Yu Zheng

Katsu Yamane

Jessica Kate Hodgins

Katsu Yumane

A control method, and a robot controller implementing the method, is provided that adapts human motions to floating-base humanoid robots with time warping techniques. The method of modifying a set of reference motions modifies the timeline of a reference motion so as to speed up or slow down one or more of the motions or motion segments. Through the use of time warping, the velocity and acceleration profiles of the motion are changed to turn an infeasible motion into a feasible one. The optimal time warping is obtained through a generalized motion feasibility index that quantifies the feasibility of a motion considering the friction constraint as well as the center-of-pressure (CoP) constraint. Due to the use of the motion feasibility index, the proposed motion adaptation method taught herein can be applied to motions on arbitrary terrains or with any number of links in contact with the environment.

Control method for floating-base robots including generating feasible motions using time warping

Techniques are disclosed for optimizing and maintaining cyclic biped locomotion of a robot on an object. The approach includes simulating trajectories of the robot in contact with the object. During each trajectory, the robot maintains balance on the object, while using the object for locomotion. The approach further includes determining, based on the simulated trajectories, an initial state of a cyclic gait of the robot such that the simulated trajectory of the robot starting from the initial state substantially returns to the initial state at an end of one cycle of the cyclic gait. In addition, the approach includes sending joint angles and joint velocities of the initial state to a set of joint controllers of the robot to cause a leg of the robot to achieve the initial state so the robot moves through one or more cycles of the cyclic gait.

Robot cyclic locomotion using a dynamic object

A controller for floating-base humanoid robots that can track motion capture data while maintaining balance. Briefly, the controller includes a proportional-derivative (PD) controller that is adapted to compute the desired acceleration to track a given reference trajectory at every degree-of-freedom (DOF) of the robot including the six unactuated ones of the floating base. Second, the controller includes a component (joint torque optimization module) that computes the optimal joint torques and contact forces to realize the desired accelerations given by the first component (i.e., the PD controller). The joint torque optimization module performs this computation considering the full-body dynamics of the robot and the constraints on contact forces. The desired accelerations may not be feasible for the robot due to limits in normal contact forces and friction (e.g., the robot sometimes cannot exactly copy or perform the modeled human motion defined by motion capture data).

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Pittsburgh, PA, United States of America

Yu Zheng