Developing social action capabilities in a humanoid robot using an interaction history architecture

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ICHR.2008.4756013We present experimental results for the humanoid robot Kaspar2 engaging in a simple “peekaboo” interaction game with a human partner. The robot develops the capability to engage in the game by using its history of interactions coupled with audio and visual feedback from the interaction partner to continually generate increasingly appropriate behaviour. The robot also uses facial expressions to feedback its level of reward to the partner. The results support the hypothesis that reinforcement of time-extended experiences through interaction allows a robot to act appropriately in an interaction

Reuse of Neural Modules for General Video Game Playing

A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1