Teaser: Contact-Rich Grasping & Locomotion via Distributed Optimization for Multi-Limbed Robots

Conference paper accepted for the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2022) in Kyoto, Japan:
Pre-print: https://arxiv.org/abs/2207.01418
Authors: Yuki Shirai, Xuan Lin, Alexander Schperberg, Yusuke Tanaka, Hayato Kato, Varit Vichathorn, and Dennis Hong

[Abstract]
While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45° with a much shorter planning time.

Видео Teaser: Contact-Rich Grasping & Locomotion via Distributed Optimization for Multi-Limbed Robots канала RoMeLa