Humanoid Loco-Manipulation Planning Based on Graph Search and Reachability Maps

2021-04-01

IEEE Robotics and Automation Letters

AI Summary

This work addresses the challenge of locomotion-manipulation co-planning for autonomous object transport by humanoid robots. We propose a graph-search-based sequential motion planning framework. Our key contributions are: (1) a novel state transition model enabling flexible coupling between gait and grasping actions; and (2) the first integration of real-time relocalization of reachability maps into the graph search process, enabling efficient online update and switching of dynamic reachable regions under robot–object cooperative motion. The method unifies kinematic modeling, multimodal motion planning, and real-time map construction. Evaluated in simulation and on a physical humanoid platform, it successfully accomplishes complex tasks—including drum rolling and mid-transport regrasping—demonstrating full autonomy, task generality, and real-time performance with millisecond-scale replanning.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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.

Problem

• Efficient planning for humanoid robot loco-manipulation tasks
• Graph search-based sequencing of footsteps and grasps
• Dynamic reachability maps for flexible motion planning

Innovation

• Graph search for footsteps and grasps planning
• Reachability maps relocation for quick evaluation
• Flexible transition model for loco-manipulation