[robotics-worldwide] [meetings] ICRA 2015 Workshop on Sensorimotor Learning

Andrea Censi censi at mit.edu
Sat Apr 18 16:39:28 PDT 2015

                    ** CALL FOR PAPERS **
        ICRA 2015 Workshop on Sensorimotor Learning
           Seattle WA, USA, May 26, 2015

Topic and Objectives

The workshop is dedicated to recent advances in sensorimotor
learning for robotics. The development of robots that are able to
learn models of themselves and their environments has long been a
goal in the robotics, machine learning, and AI communities.
However, most current approaches to robot sensing and control are
based on strong prior assumptions, which make them brittle to
unmodeled dynamics and unexpected changes in the robot body or the
environment. Advances in machine learning, including deep learning,
nonparametric modeling and inference, and reinforcement learning
have recently experienced success in deriving models and policies
directly from data. For example, in computer vision, deep learning
methods, which learn “everything” from data, including low-level
features and intermediate representations, have surpassed
traditional approaches in accuracy on problems such as object
detection and classification. However, incorporating modern machine
learning techniques into real-world sensorimotor systems is still
challenging. Most real-world sensorimotor control problems are
situated in continuous or high-dimensional environments and require
real-time interaction, which can be problematic for classical
learning techniques. In order to overcome these difficulties, the
modeling, learning, and planning components of a fully adaptive
decision making system may need significant modifications. This
workshop’s goal is to foster discussion on these issues, especially
with the participation of the machine learning and computational
biology community.

We would like the workshop to be as inclusive as possible
and encourage paper submissions and participation from
a wide range of research related to sensorimotor learning,
including machine learning and computational biology.
High-level questions to be addressed include:

- Is it possible to learn the “torque-to-pixels” high-dimensional
  sensorimotor dynamics of robots or animals directly from the
  raw data? If not, what prior knowledge is necessary?

- What are the challenges for high-dimensional cross-modal
  sensorimotor learning in robotics?

- Can cross-model models be learned independently of a task?

- How can we transfer biological insights to robotic systems
  (and vice versa)?

- Do engineering insights in machine learning and robotics have
  a biological explanation?

- How can one balance the representation accuracy
  and the speed of inference?

- How can online machine learning be used in high-frequency
  control of real-world systems?

- How can successful supervised or unsupervised learning
  techniques be used in sensorimotor control problems?

- How can prior knowledge, including expert knowledge,
  user demonstrations, or distributional assumptions be
  incorporated into the learning/planning framework?

- What lessons can be learned across disciplines between
  the control, neuroscience, and reinforcement learning
 communities, especially in their use of learning models?

Important Dates

Paper submission: Friday May 1st, 2015
Notification of acceptance: Monday May 4th, 2015
Workshop Date: May 26, 2015 (the first day of ICRA)

Invited talks

Ben Kuipers, University of Michigan, USA
Russ Salakhutdinov, University of Toronto, Canada
Marianna Madry, Royal Institute of Technology (KTH), Sweden
Sergey Levine, University of California, Berkeley, USA


We encourage submission of original work and late-breaking results.
Please submit a 2 page abstract (formatted with the ICRA template).
It is also OK to submit work that is submitted or accepted elsewhere;
in this case, please indicate where the work has been submitted or accepted.

Please send all submissions to: censi at mit.edu
(Submission deadline: Friday May 1st, 2015)

Any additional questions can be directed to the workshop organizers.

Organization Chairs

Byron Boots
Georgia Institute of Technology
bboots at cc.gatech.edu

Andrea Censi
Massachusetts Institute of Technology
censi at mit.edu

Andrea Censi | LIDS / MIT | http://censi.mit.edu

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