[IROS21] Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective

Abstract

We present a Virtual Kinematic Chain (VKC) perspective, a simple yet effective method, to improve task planning efficacy for mobile manipulation. By consolidating the kinematics of the mobile base, the arm, and the object being manipulated collectively as a whole, this novel VKC perspective naturally defines abstract actions and eliminates unnecessary predicates in describing intermediate poses. As a result, these advantages simplify the design of the planning domain and significantly reduce the search space and branching factors in solving planning problems. In experiments, we implement a task planner using Planning Domain Definition Language (PDDL) with VKC. Compared with conventional domain definition, our VKC-based domain definition is more efficient in both planning time and memory. In addition, abstract actions perform better in producing feasible motion plans and trajectories. We further scale up the VKC-based task planner in complex mobile manipulation tasks. Taken together, these results demonstrate that task planning using VKC for mobile manipulation is not only natural and effective but also introduces new capabilities.

Publication
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
Song-Chun Zhu
Song-Chun Zhu
Chair Professor
Yixin Zhu
Yixin Zhu
Assistant Professor

I build humanlike AI.

Hangxin Liu
Hangxin Liu
Research Scientist

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