ArticleFrontiers in robotics and AI2020
Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition.
Article in Frontiers in robotics and AI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment.Minds and machines · 2023Article
- Honeybee communication during collective defence is shaped by predation.BMC biology · 2021Article
- On the convergence of projective-simulation-based reinforcement learning in Markov decision processes.Quantum machine intelligence · 2020Article
- A Stochastic Process Model for Free Agency under Indeterminism.Dialectica (Bern, Switzerland) · 2018Article
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Authors and funding
4 authors.
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Abstract
We consider the problem of autonomous acquisition of manipulation skills where problem-solving strategies are initially available only for a narrow range of situations. We propose to extend the range of solvable situations by autonomous play with the object. By applying previously-trained skills and behaviors, the robot learns how to prepare situations for which a successful strategy is already known. The information gathered during autonomous play is additionally used to train an environment model. This model is exploited for active learning and the generation of novel preparatory behaviors compositions. We apply our approach to a wide range of different manipulation tasks, e.g., book grasping, grasping of objects of different sizes by selecting different grasping strategies, placement on shelves, and tower disassembly. We show that the composite behavior generation mechanism enables the robot to solve previously-unsolvable tasks, e.g., tower disassembly. We use success statistics gained during real-world experiments to simulate the convergence behavior of our system. Simulation experiments show that the learning speed can be improved by around 30% by using active learning.
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