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Task-Motion Planning with Reinforcement Learning for Adaptable Mobile Service Robots.
Yuqian
Jiang, Fangkai Yang, Shiqi
Zhang, and Peter Stone.
In Proceedings of the IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2019), November 2019.
Task-motion planning (TMP) addresses the problem of efficiently generating executable and low-cost task plans in a discrete space such that the ( initially unknown) action costs are determined by motion plans in a corresponding continuous space. A task-motion plan for a mobile service robot that behaves in a highly dynamic domain can be sensitive to domain uncertainty and changes, leading to suboptimal behaviors or execution failures. In this paper, we propose a novel framework, TMP-RL, which is an integration of TMP and reinforcement learning (RL), to solve the problem of robust TMP in dynamic and uncertain domains. The robot first generates a low-cost, feasible task-motion plan by iteratively planning in the discrete space and updating relevant action costs evaluated by the motion planner in continuous space. During execution, the robot learns via model-free RL to further improve its task-motion plans. RL enables adaptability to the current domain, but can be costly with regards to experience; using TMP, which does not rely on experience, can jump-start the learning process before executing in the real world. TMP-RL is evaluated in a mobile service robot domain where the robot navigates in an office area, showing significantly improved adaptability to unseen domain dynamics over TMP and task planning (TP)-RL methods.
@InProceedings{IROS19-jiang,
author = {Yuqian Jiang and Fangkai Yang and Shiqi Zhang and Peter Stone},
title = {Task-Motion Planning with Reinforcement Learning for Adaptable Mobile Service Robots},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)},
location = {Macau, China},
month = {November},
year = {2019},
abstract = {
Task-motion planning (TMP) addresses the problem of efficiently generating
executable and low-cost task plans in a discrete space such that the (
initially unknown) action costs are determined by motion plans in a
corresponding continuous space. A task-motion plan for a mobile service
robot that behaves in a highly dynamic domain can be sensitive to domain
uncertainty and changes, leading to suboptimal behaviors or execution
failures. In this paper, we propose a novel framework, TMP-RL, which is an
integration of TMP and reinforcement learning (RL), to solve the problem of
robust TMP in dynamic and uncertain domains. The robot first generates a
low-cost, feasible task-motion plan by iteratively planning in the discrete
space and updating relevant action costs evaluated by the motion planner in
continuous space. During execution, the robot learns via model-free RL to
further improve its task-motion plans. RL enables adaptability to the
current domain, but can be costly with regards to experience; using TMP,
which does not rely on experience, can jump-start the learning process
before executing in the real world. TMP-RL is evaluated in a mobile service
robot domain where the robot navigates in an office area, showing
significantly improved adaptability to unseen domain dynamics over TMP and
task planning (TP)-RL methods.
},
}
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