3/28/2023 0 Comments Oneshot guide rowbot![]() ![]() Our model estimates the O-space according to the F-formation theory. Keywords: Social Intelligence for Robots, Creating Human-Robot Relationships, Cooperation and Collaboration in Human-Robot TeamsĪbstract: In this paper, we present a model to propose an optimal placement for a robot in a social group interaction. Pathi, Sai Krishna (Örebro University), Kristofferson, Annica (Mälardalen University), Kiselev, Andrey (Orebro University), Loutfi, Amy (Örebro University) We release Space Fortress as an open-source Gym environment.Įstimating Optimal Placement for a Robot in Social Group Interaction We also identify independent axes along which to vary context and temporal sensitivity, allowing Space Fortress to be used as a testbed for understanding both characteristics in combination and also in isolation. We show that existing state-of-the-art RL algorithms are unable to learn to play the Space Fortress game, and then confirm that this poor performance is due to the RL algorithms' context insensitivity. This paper introduces the game of Space Fortress as a RL benchmark which specifically targets these characteristics. As a result, research in RL has not given these challenges their due, resulting in algorithms which do not understand critical changes in context, and have little notion of real world time. However, these benchmarks do not emphasize two important characteristics that are often present in real-world domains: requirement of changing strategy conditioned on latent contexts, and temporal sensitivity. Keywords: Machine Learning and Adaptation, Evaluation Methods and New MethodologiesĪbstract: Research in deep reinforcement learning (RL) has coalesced around improving performance on benchmarks like the Arcade Learning Environment. Learning Context-Sensitive Strategies in Space FortressĪgarwal, Akshat (Carnegie Mellon University), Hope, Ryan (Carnegie Mellon University), Sycara, Katia (Carnegie Mellon University) ![]() We show that our method is able to improve the average received reward significantly in comparison to the other state-of-the-art methods. The strategy is learnt in simulation, using a simulated human opponent and an ideal robot that can perform hitting motion in its workspace accurately. The cooperative learning framework of Kohenon Self Organizing Map (KSOM) along with Replay Memory is employed for faster strategy learning in this short horizon problem. This paper presents a novel technique to learn a higher level strategy for the game of table tennis using P-Q Learning (a mixture of Pavlovian learning and Q-learning) to learn a parameterized policy. In this paper, we address this very important problem on how to learn the higher level optimal strategies that enable competitive behaviour with humans in such an interactive game setting. Decision making is a major part of an intelligent robot and a policy is needed to choose and execute the action which receives highest reward. Accurate dynamic trajectory generation in such dynamic situations and an appropriate controller in order to respond to the incoming table tennis ball from the opponent is only a prerequisite to win the game. Keywords: Machine Learning and Adaptation, Social Learning and Skill Acquisition Via Teaching and Imitation, Motivations and Emotions in RoboticsĪbstract: Complex and interactive robot manipulation skills such as playing a game of table tennis against a human opponent is a multifaceted challenge and a novel problem.
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