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Coaching Robots: Online Behavior Learning from Human Subjective Feedback

  • Masakazu Hirkoawa
  • Kenji Suzuki
Part of the Studies in Computational Intelligence book series (SCI, volume 442)

Abstract

This chapter describes a novel methodology for behavior learning of an agent, called Coaching. The proposed method is an interactive and iterative learning method which allows a human trainer to give a subjective evaluation to the robotic agent in real time, and the agent can update the reward function dynamically based on this evaluation simultaneously. We demonstrated that the agent is capable of learning the desired behavior by receiving simple and subjective instructions such as positive and negative. The proposed approach is also effective when it is difficult to determine a suitable reward function for the learning situation in advance. We have conducted several experiments with a simulated and a real robot arm system, and the advantage of the proposed method is verified throughout those experiments.

Keywords

Reinforcement Learning Humanoid Robot Radial Basis Function Network Target Behavior Inverted Pendulum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dept. of Intelligent Interaction TechnologiesUniversity of TsukubaTsukubaJapan
  2. 2.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan

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