Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot.

ABSTRACT: Buzzwire tasks are often used as benchmarks and as training environments for fine motor skills and high precision path following. These tasks require moving a wire loop along an arbitrarily shaped wire obstacle in a collision-free manner. While there have been some demonstrations of buzzwire tasks with robotic manipulators using reinforcement learning and admittance control, there does not seem to be any examples with humanoid robots. In this work, we consider the scenario where we control one arm of the REEM-C humanoid robot, with other joints fixed, as groundwork for eventual full-body control. In pursuit of this, we contribute by designing an optimal control problem that generates trajectories to solve the buzzwire in a time optimized manner. This is composed of task-space constraints t

SUBMITTER: Lee PQ 

PROVIDER: S-EPMC9203844 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature