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Deep R-Learning for Continual Area Sweeping (2020)
Rishi Shah and
Yuqian Jiang
and
Justin Hart
and
Peter Stone
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics. We also present a video demonstration on a real service robot.
View:
PDF
Citation:
In
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)
, Las Vegas, NV, USA, October 2020.
Bibtex:
@article{IROS20-shah, title={Deep R-Learning for Continual Area Sweeping}, author={Rishi Shah and Yuqian Jiang and Justin Hart and Peter Stone}, booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)}, month={October}, address={Las Vegas, NV, USA}, url="http://nn.cs.utexas.edu/?IROS20-shah", year={2020} }
Presentation:
Slides (PDF)
People
Justin Hart
hart [at] cs utexas edu
Yuqian Jiang
Peter Stone
pstone [at] cs utexas edu
Areas of Interest
Machine Learning
Service Robots
Reinforcement Learning
Robotics