Humanoid Loco-manipulation Planning based on Graph Search and Reachability Maps

TL;DR

This paper introduces a novel loco-manipulation planning method for humanoid robots that uses graph search and reachability maps to efficiently plan footstep and grasp sequences for object transportation.

Contribution

It presents a new transition model and planning framework that enables flexible and automatic loco-manipulation planning for humanoid robots.

Findings

Successfully planned a bobbin rolling operation with regrasping

Demonstrated efficient and versatile loco-manipulation planning

Validated approach through practical use-case scenarios

Abstract

In this letter, we propose an efficient and highly versatile loco-manipulation planning for humanoid robots. Loco-manipulation planning is a key technological brick enabling humanoid robots to autonomously perform object transportation by manipulating them. We formulate planning of the alternation and sequencing of footsteps and grasps as a graph search problem with a new transition model that allows for a flexible representation of loco-manipulation. Our transition model is quickly evaluated by relocating and switching the reachability maps depending on the motion of both the robot and object. We evaluate our approach by applying it to loco-manipulation use-cases, such as a bobbin rolling operation with regrasping, where the motion is automatically planned by our framework.