--------------------------------------------------------------------------------------------- Call for Contributions --------------------------------------------------------------------------------------------- Are the skeptics right? Limits and Potentials of Deep Learning in Robotics
Workshop at Robotics: Science and Systems (RSS) June 18, 2016 --- Ann Arbor, Michigan, USA Website: http://Juxi.net/workshop/deep-learning-rss-2016/ <http://juxi.net/workshop/deep-learning-rss-2016/> Submission via eMail to deep-learn...@roboticvision.org <mailto:deep-learn...@roboticvision.org> =========================================== We invite contributions spanning the areas of deep learning, computer vision and robotics. The workshop's programme is complemented by contributed research papers that will be presented with lightning talks and in a poster session. We explicitly encourage the submission of papers describing work in progress, or containing preliminary results to discuss with the community. Submissions should follow the usual RSS guidelines for style and length (up to 8 pages). The papers will be reviewed by the workshop organisers and accepted papers will be published on the workshop website. In addition we encourage the community to submit questions for the speakers and panel before the workshop via our web form. We hope to stimulate an interactive discussion this way. Important Dates: Submission Deadline: May 8, 2016 (anywhere on the planet) Acceptance Notification: May 15, 2016 Workshop Date: June 18, 2016 Workshop Description: Deep learning techniques have revolutionised many aspects of computer vision over the past three years and have been tremendously successful at tasks like object recognition and detection, scene classification, action recognition, and caption generation. Despite deep learning thriving in computer vision, it has not yet been nearly as impactful in robotic vision. Although deep learning techniques are successfully applied by a few groups for tasks like visually guided robotic grasping and manipulation, they have not yet evolved into mainstream approaches that are generally adopted and applied. Furthermore, many renowned robotics researchers are outspoken sceptics and have questioned the applicability of deep learning techniques for various robotic scenarios in workshops and informal discussions during RSS, ICRA, and other venues during the past year. In this workshop, world-renowned experts from the robotics, deep learning, and computer vision communities will elaborate on what limits the applicability of deep learning in various robotics scenarios, but also highlight the potential of deep learning where it has been successfully applied in robotics. The talks will outline open research questions that should be tackled by the community to overcome the identified limits. Furthermore, they will identify the key differences in the paradigms underlying typical applications in robotics and other areas where deep learning thrives. In a panel discussion with the invited experts and the audience, the workshop participants will further refine the proposed directions for future research. To engage the wider research community we invite researchers to send us their questions to the invited speakers and the panelists to discuss during the workshop via the following online form: https://goo.gl/2YHpqE <https://docs.google.com/forms/d/162DKikrgd4Sy-kORfi2ESlKBoxJf258FVRsLqfaue1Q/viewform?c=0&w=1&usp=mail_form_link> Topic areas: The topics of interest for contributed papers comprise, but are not limited to: limits of deep learning for robotics case studies: when does state-of-the-art deep learning fail in robotics? success stories: where did deep learning enable breakthroughs in robotics? fundamental differences between typical computer vision tasks and robotic vision deep learning for perception, action, and control in robotics contexts reliable confidence measures for deep classifiers exploitation of semantic information and prior knowledge for deep learning deep learning in the context of open set classification incremental learning, incorporation of human feedback for classification utilizing robotic technology to create novel datasets comprising interaction, active vision etc. deep learning for embedded systems or platforms with limited computational power Invited Speakers: Wolfram Burgard, Freiburg University Pieter Abbeel, UC Berkeley Ashutosh Saxena, Stanford University Dieter Fox, Washington University John Leonard, MIT Oliver Brock, TU Berlin Walter Scheirer, University of Notre Dame Raia Hadsell, Google DeepMind Organizers: Dr Niko Sünderhauf, Dr Jürgen Leitner, Assoc Prof Michael Milford, Assoc Prof Ben Upcroft, Prof Peter Corke Australian Centre for Robotic Vision (ACRV), Queensland University of Technology (QUT), Australia Assoc Prof Pieter Abbeel UC Berkeley, USA Prof Wolfram Burgard Cluster of Excellence BrainLinks-BrainTool, Universität Freiburg, Germany -- Juxi Leitner | http://Juxi.net <http://juxi.net/> Research Fellow | ARC Centre of Excellence for Robotic Vision E: j.leit...@qut.edu.au <mailto:j.leit...@qut.edu.au> | W: www.roboticvision.org <http://www.roboticvision.org/> | P: +61 7 3138 2363 Science and Engineering Faculty | Queensland University of Technology Gardens Point, S Block 11 30 | 2 George Street, Brisbane, QLD 4000 | CRICOS No. 00213J
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