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View all- Guo HWu FQin YLi RLi KLi K(2023)Recent Trends in Task and Motion Planning for Robotics: A SurveyACM Computing Surveys10.1145/358313655:13s(1-36)Online publication date: 13-Jul-2023
Closed-Loop Global Motion Planning for Reactive, Collision-Free Execution of Learned Tasks
Article No.: 10, Pages 1 - 16
Abstract
We present a robot motion planning approach for performing a learned task while reacting to the movement of obstacles and task-relevant objects. We employ a closed-loop, sampling-based motion planner operating multiple times a second that senses obstacles and task-relevant objects and generates collision-free motion plans based on a learned-task model. The task model is learned from expert demonstrations prior to task execution and is represented as a hidden Markov model. During task execution, our motion planner quickly searches in the Cartesian product of the task model and a probabilistic roadmap for a collision-free plan with features most similar to the demonstrations given the current locations of the task-relevant objects. We accelerate replanning using a fast bidirectional search and by biasing the sampling distribution using information from the learned-task model. We show the efficacy of our approach with the Baxter robot performing two tasks.
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Index Terms
- Closed-Loop Global Motion Planning for Reactive, Collision-Free Execution of Learned Tasks
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Information
Published In
May 2018
100 pages
EISSN:2573-9522
DOI:10.1145/3223875
- Editors:
- Odest Chadwicke Jenkins,
- Selma Sabanovic
Copyright © 2018 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 May 2018
Accepted: 01 February 2018
Received: 01 October 2017
Published in THRI Volume 7, Issue 1
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- NSF Computing and Communication Foundations
- NSF Information & Intelligent Systems
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View all- Guo HWu FQin YLi RLi KLi K(2023)Recent Trends in Task and Motion Planning for Robotics: A SurveyACM Computing Surveys10.1145/358313655:13s(1-36)Online publication date: 13-Jul-2023
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