期刊
FRONTIERS IN ROBOTICS AND AI
卷 6, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/frobt.2019.00151
关键词
data-efficient robot learning; model-based learning; repertoire-based robot learning; evolutionary robotics; fault tolerance in robotics
类别
资金
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [637972]
- Chist-Era project HEAP
- Lifelong Learning Machines program (L2M) from DARPA/MTO [FA8750-18-C-0103]
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.
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