Human-Robot Collaborative Multi-Agent Path Planning using Monte Carlo Tree Search and Social Reward Sources
| Autor: | Jose Enrique Dominguez, Pablo Jiménez, Alberto Sanfeliu, Anais Garrell, Marc Dalmasso |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents, Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Interface (Java) Computer science Monte Carlo tree search Monte Carlo method 02 engineering and technology Human–robot interaction Task (project management) 020901 industrial engineering & automation Interacció persona-robot 11. Sustainability Mobile robots Motion planning Human-robot collaboration business.industry Mobile robotics Navigation Planning Robots mòbils Robot Probability distribution Artificial intelligence Collaborative search Human-robot interaction Informàtica::Robòtica [Àrees temàtiques de la UPC] business |
| Zdroj: | ICRA UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Digital.CSIC. Repositorio Institucional del CSIC instname |
| DOI: | 10.1109/icra48506.2021.9560995 |
| Popis: | Trabajo presentado en el International Conference on Robotics and Automation (ICRA), celebrado de forma híbrida (virtual y presencial), desde Xian (China), del 30 de mayo al 5 de junio de 2021 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. |
| Databáze: | OpenAIRE |
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