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Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration

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As we expect robots to start moving from working in isolated industry settings into human populated environments, our need to develop suitable learning algorithms for the latter increases. Human-robot collaboration is a particular area that has tremendous gains from endowing a robot with such learning capabilities, focusing on robots that can work side-by-side with a human and provide supportive behaviours throughout a task executed by the human worker. In this paper, we propose a framework based on hierarchical multi-agent reinforcement learning that considers the human as an “expert” agent in the system—an agent whose actions we cannot control but whose actions, jointly with the robot’s actions, impact the state of the task. Our framework aims to provide the learner (the robot) with a way of learning how to provide supportive behaviours to the expert agent (the person) during a complex task. The robot employs communicative actions to interactively learn from the expert agent at key points during the task. We use a hierarchical approach in order to integrate the communicative actions in the multi-agent reinforcement learning framework and allow for simultaneously learning the quality of performing different supportive behaviours for particular combinations of task states and expert agent actions. In this talk, we present our proposed framework, detail the motion capture system data collection we performed in order to learn about the task states and characterise the expert agent’s actions, and discuss how we can apply the framework to our human-robot collaboration scenario.

This talk is part of the Machine Learning @ CUED series.

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