Online, interactive user guidance for high-dimensional, constrained motion planning

TL;DR

This paper introduces an interactive user-guided planning framework for high-dimensional robots, enabling efficient pathfinding with minimal domain knowledge by leveraging user input only when the planner stalls.

Contribution

The authors propose a novel framework where user guidance is integrated dynamically during planning, improving high-dimensional, constrained pathfinding without pre-designed heuristics.

Findings

Enables high-dimensional, constrained path planning with minimal domain knowledge.

User guidance improves planning efficiency when the algorithm stalls.

Applicable to complex robots like a 34-DOF humanoid.

Abstract

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics.