Conference proceeding
Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 8288-8294
27 Sep 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective
- Creators
- Ziyuan Jiao - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentZeyu Zhang - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentWeiqi Wang - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentDavid Han - Drexel University,Department of Electrical and Computer EngineeringSong-Chun Zhu - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentYixin Zhu - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentHangxin Liu - UCLA Center for Vision, Cognition,Learning, and Autonomy (VCLA) at Statistics DepartmentIEEE
- Publication Details
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 8288-8294
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000755125506074
- Scopus ID
- 2-s2.0-85113434834
- Other Identifier
- 991019169802804721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Robotics