Probabilistic Policy Blending for Shared Autonomy using Deep Reinforcement Learning

Technologies in machine learning and artificial intelligence have come a long way in decision making and system automation, but still faces difficult challenges in semi-automation and human-in-the-loop frameworks. This work presents a probabilistic policy blending approach for shared control between a human operator and an intelligent agent. The proposed approach assumes that the agent can control a system and the human operator needs to communicate the system’s intended goal. A comparative study is presented between different arbitration functions that are used to blend the human and agent’s actions. The proposed approach can achieve a variable level of assistance to the human operator successfully within discrete action space using the Lunar Lander game environment developed by OpenAI. Furthermore, human physiological data have been analyzed while the human interacts with the system and the agent using different arbitration functions. A correlation between the physiological data, arbitration level, and task performance was observed.

Recommended citation: S. Singh and J. Heard, “Probabilistic Policy Blending for Shared Autonomy using Deep Reinforcement Learning,” 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 1537-1544.
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