Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions

Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions Jonas Tjomsland 1, Ali Shafti 1,2,3, A. Aldo Faisal 1,2,3,4 1 Dept. of Bioengineering, 2 Dept. of Computing, 3 Data Science Institute, 4 UKRI CDT for AI in Healthcare, Imperial College London jt732@cam.ac.uk, a.shafti@imperial.ac.uk, aldo.faisal@imperial.ac.uk Abstract W e present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning. Leveraging the sample-efficiency of the Soft Actor-Critic algorithm, we have implemented a robotic platform able to learn a nontrivial collaborative task with a human partner, without pre-training in simulation, and using only 30 minutes of real-world interactions. This enables us to study Human-Robot and Human-Human collaborative learning through real-world interactions. W e present preliminary results, showing that state-of-the-art deep learning methods can take human-robot collaborative learning a step closer to that of humans interacting with each other . 1 Introduction Artificially intelligent agents are displaying impressive behaviour in diverse individual tasks, such as skin cancer classification [1] and complex board games [2]. Similarly, multi-agent environments, where a degree of teamwork is required, are being explored [3].