Last Call! Robotics Science and Systems, June 27, Berlin Workshop on Active learning in robotics: Exploration, Curiosity, and Interaction
Workshop description Applications of robots are expanding at a fast rate and are expected to operate in less controllable and harder to model domains. Learning and adaptation becomes essential to deploy robots that continuously interact with the environment, acquire new data during operation and use them to improve its performance by developing new skills or improving and adapting its models. How should a robot acquire and use this stream of data? How can it close the action-perception loop to efficiently learn models and acquire skills? Researchers in robotics, statistics and machine learning have answered these questions from different perspectives and setups: active learning, submodular optimization, exploration strategies, multi-armed bandits among many others. All such approaches provide ways for the robot to choose better data to learn, reducing the time and energy used while at the same time improving generalization capabilities. The goal of this workshop is to show how formalisms developed in different communities can be applied in a multidisciplinary context as it is robotics research. It will bring together researchers to build bridges between these different perspectives and to exchange ideas about representations and methods for active learning in robotics. In addition to the classical exploration problem, the workshop will also explore connections with new trends such as using intrinsic motivation to model curiosity and drive exploration towards the acquisition of unknown skills or the development of active strategies for human-robot interaction in the context of co-working or learning from a human teacher. Keywords: Active Learning, Reinforcement Learning, Markov Decision Processes, Exploration/Exploitation, Intrinsic Motivation Call for contributions: We solicit contributed presentations in all areas of active learning applied in robotics including, but not limited to: exploration, reinforcement learning, active sensing and perception, intrinsic motivation, active manipulation, human-robot co-working. The workshop aims to foster discussion between the different active strategies. Therefore, we will accept already published materials as well as unpublished work. Contributions will be evaluated in terms of its relevance to the workshop topic. Accepted contributions will be given an oral presentation or poster (plus spotlight talk) at the workshop. Important dates: Paper Submission: May 7, 2013 Paper Notification: May 15, 2013 Workshop Dates: June 27, 2013 Submission can be done at: https://www.easychair.org/conferences/?conf=rss13-ws-alr See webpage for more details: https://webdiis.unizar.es/~montesan/web/index.php/rss2013wsactivelearning --------------------------------------------------------------- Manuel Lopes - Researcher flowers.inria.fr/mlopes FLOWERS team Phone: +33524574179 200, avenue de la Vieille Tour 33405 Talence, Cedex, France --------------------------------------------------------------- _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
