AUTHOR=Kaushik Rituraj , Desreumaux Pierre , Mouret Jean-Baptiste TITLE=Adaptive Prior Selection for Repertoire-Based Online Adaptation in Robotics JOURNAL=Frontiers in Robotics and AI VOLUME=6 YEAR=2020 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2019.00151 DOI=10.3389/frobt.2019.00151 ISSN=2296-9144 ABSTRACT=

Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e.g., a damaged robot, a new object, etc.). In this paper, we relax the assumption of previous works that a single repertoire is enough for adaptation. Instead, we generate repertoires for many different situations (e.g., with a missing leg, on different floors, etc.) and let our algorithm selects the most useful prior. Our main contribution is an algorithm, APROL (Adaptive Prior selection for Repertoire-based Online Learning) to plan the next action by incorporating these priors when the robot has no information about the current situation. We evaluate APROL on two simulated tasks: (1) pushing unknown objects of various shapes and sizes with a robotic arm and (2) a goal reaching task with a damaged hexapod robot. We compare with “Reset-free Trial and Error” (RTE) and various single repertoire-based baselines. The results show that APROL solves both the tasks in less interaction time than the baselines. Additionally, we demonstrate APROL on a real, damaged hexapod that quickly learns to pick compensatory policies to reach a goal by avoiding obstacles in the path.