Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots

TL;DR

This paper introduces a distributed optimization framework for multi-limbed robots that enables simultaneous contact-rich grasping and locomotion, including free-climbing, with improved planning efficiency and real-world validation.

Contribution

It presents a novel distributed optimization approach combining MIQP and NLP for integrated motion and contact planning in multi-limbed robots, enabling complex maneuvers like free-climbing.

Findings

Successful hardware demonstration of free-climbing at 45° slope.

Significantly reduced planning time compared to traditional methods.

Effective enforcement of patch contact constraints from limit surfaces.

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{\deg} with a much shorter planning time.