|Organizers:||Jan Peters, Marc Toussaint|
|Date:||December 7, 2007|
|Room:||Hilton: Black Tusk (90)|
|Location:||Westin Resort and Spa and Westin Hilton, Whistler, B.C., Canada|
|Program:||Official program as PDF|
|Abstracts:||All abstracts as PDF|
Creating autonomous robots that can assist humans in situations of daily life is a great challenge for machine learning. While this aim has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences, we have yet to achieve the first step of creating robots that can accomplish a multitude of different tasks, triggered by environmental context or higher level instruction. Despite the wide range of machine learning problems encountered in robotics, the main bottleneck towards this goal has been a lack of interaction between the core robotics and the machine learning communities. To date, many roboticists still discard machine learning approaches as generally inapplicable or inferior to classical, hand-crafted solutions. Similarly, machine learning researchers do not yet acknowledge that robotics can play the same role for machine learning which for instance physics had for mathematics: as a major application as well as a driving force for new ideas, algorithms and approaches.
Some fundamental problems we encounter in robotics that equally inspire current research directions in Machine Learning are:
Robotics challenges can inspire and motivate new Machine Learning research as well as being an interesting field of application of standard ML techniques.
Inversely, with the current rise of real, physical humanoid robots in robotics research labs around the globe, the need for machine learning in robotics has grown significantly. Only if machine learning can succeed at making robots fully adaptive, it is likely that we will be able to take real robots out of the research labs into real, human inhabited environments. To do so, we future robots will need to be able to make proper use of perceptual stimuli such as vision, proprioceptive & tactile feedback and translate these into motor commands.
To close this complex loop, machine learning will be needed on various stages ranging from sensory-based action determination over high-level plan generation to motor control on torque level. Among the important problems hidden in these steps are problems which can be understood from the robotics and the machine learning point of view including perceptuo-action coupling, imitation learning, movement decomposition, probabilistic planning problems, motor primitive learning, reinforcement learning, model learning and motor control.
The goal of this workshop is to bring together people that are interested in robotics as a source and inspiration for new Machine Learning challenges, or which work on Machine Learning methods as a new approach to robotics challenges. In the robotics context, among the questions which we intend to tackle are
Reinforcement Learning, Imitation, and Active Learning:
Motor Representations and Control:
Learning structured models and representations:
|Morning session: 7:30am�10:30am|
|7:30am||Welcome, Jan Peters, Max Planck Institute, Marc Toussaint, Technical University of Berlin|
|7:35am||Learning Nonparametric Policies by Imitation, David Grimes and Rajesh Rao, University of Washington|
|8:05am||Machine learning for developmental robotics, Manuel Lopes, Luis Montesano, Francisco Melo, Instituto Superior Tecnico|
|8:15am||Machine Learning Application to Robotics and Human-Robot Interaction, Aude Billard, EPFL|
|9:20am||Bayesian Reinforcement Learning in Continuous POMDPs with Application to Robot Navigation, Stephane Ross, Joelle Pineau, McGill University|
|9:50am||Self-Supervised Learning from High-Dimensional Data for Autonomous Offroad Driving, Ayse Naz Erkan, Raia Hadsell, Pierre Sermanet, Koray Kavukcuoglu, Marc-Aurelio Ranzato, Urs Muller, Yann LeCun, NYU|
|10:00am||Task-based motion primitives for the control and analysis of anthropomorphic systems, Oussama Khatib & Luis Sentis, Stanford University|
|Afternoon session: 3:30pm�6:30pm|
|3:30am||STAIR: The STanford Artificial Intelligence Robot project, Andrew Ng, Stanford University|
|4:00am||Robot Perception Challenges for Machine Learning, Chieh-Chih Wang, National Taiwan University|
|4:10am||Probabilistic inference methods for nonlinear, non-Gaussian, hybrid control, Nando de Freitas, University of British Columbia|
|5:00am||A new mathematical framework for optimal choice of actions, Emo Todorov, UCSD|
Please note that these posters were selected out of 42 poster submissions to this workshop using a rigorous reviewing process. We apologize that we did not have more space for posters as there were many other great submissions.
We recommend a poster size of 3' x 4'.
This workshop will bring together researchers from both the robotics and machine learning in order to explore how to approach the topic of solving the current statistical learning challenges in robotics in a principled way. Participants of the workshop (inclusive of the audience) are encouraged to actively participate by responding with questions and comments about the talks and give stand-up talks. Please contact the organizers if you would like to reserve apriori some time for expressing your view on a particular topic.