Original Research ARTICLE
Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots
- IDSIA, Dalle Molle Institute for Artificial Intelligence, Università della Svizzera Italiana-Scuola Universitaria Professionale della Svizzera Italiana (USI-SUPSI), Lugano, Switzerland
A reinforcement learning agent that autonomously explores its environment can utilize a curiosity drive to enable continual learning of skills, in the absence of any external rewards. We formulate curiosity-driven exploration, and eventual skill acquisition, as a selective sampling problem. Each environment setting provides the agent with a stream of instances. An instance is a sensory observation that, when queried, causes an outcome that the agent is trying to predict. After an instance is observed, a query condition, derived herein, tells whether its outcome is statistically known or unknown to the agent, based on the confidence interval of an online linear classifier. Upon encountering the first unknown instance, the agent “queries” the environment to observe the outcome, which is expected to improve its confidence in the corresponding predictor. If the environment is in a setting where all instances are known, the agent generates a plan of actions to reach a new setting, where an unknown instance is likely to be encountered. The desired setting is a self-generated goal, and the plan of action, essentially a program to solve a problem, is a skill. The success of the plan depends on the quality of the agent's predictors, which are improved as mentioned above. For validation, this method is applied to both a simulated and real Katana robot arm in its “blocks-world” environment. Results show that the proposed method generates sample-efficient curious exploration behavior, which exhibits developmental stages, continual learning, and skill acquisition, in an intrinsically-motivated playful agent.
Keywords: intrinsic motivation, artificial curiosity, continual learning, developmental robotics, online active learning, markov decision processes, AI planning, systematic exploration
Citation: Ngo H, Luciw M, Förster A and Schmidhuber J (2013) Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots. Front. Psychol. 4:833. doi: 10.3389/fpsyg.2013.00833
Received: 12 July 2013; Accepted: 21 October 2013;
Published online: 26 November 2013.
Edited by:Tom Stafford, University of Sheffield, UK
Copyright © 2013 Ngo, Luciw, Förster and Schmidhuber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hung Ngo, IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland e-mail: firstname.lastname@example.org