Publication
Human-robot collaborative multi-agent path planning using Monte Carlo tree search and Social Reward Sources
Conference Article
Conference
IEEE International Conference on Robotics and Automation (ICRA)
Edition
2021
Pages
10133-10138
Doc link
https://doi.org/10.1109/ICRA48506.2021.9560995
File
Authors
Projects associated
Abstract
The collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, a multiagent plan for both itself and the human. Then, both plans are submitted to human scrutiny, who either agrees or modifies it forcing the robot to adapt its own restrictions or preferences. This process is repeated along the search task as many times as required by the human. Our planner is based on a decentralized variant of Monte Carlo Tree Search (MCTS), with one robot and one human as agents. Moreover, our algorithm allows the robot and the human to optimize their own actions by maintaining a probability distribution over the plans in a joint action space. The method allows an objective function definition over action sequences, it assumes intermittent communication, it is anytime and suitable for on-line replanning. To test it, we have developed a human-robot communication mobile phone interface. Validation is provided by real-life search experiments of a Parcheesi token in an urban space, including also an acceptability study.
Categories
automation.
Author keywords
human-robot interaction, human-robot collaboration, collaborative search, navigation, mobile robotics, planning
Scientific reference
M. Dalmasso, A. Garrell Zulueta, J.E. Domínguez, P. Jiménez and A. Sanfeliu. Human-robot collaborative multi-agent path planning using Monte Carlo tree search and Social Reward Sources, 2021 IEEE International Conference on Robotics and Automation, 2021, Xian, China, pp. 10133-10138.
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