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

Andrea Censi censi at mit.edu
Sun May 17 13:51:20 PDT 2015

ICRA 2015 Workshop on Sensorimotor Learning
Seattle WA, USA, May 26, 2015


The workshop is dedicated to recent advances in sensorimotor
learning and control 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, control,
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.

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 control,
machine learning and computational biology.

High-level questions to be addressed include, but are not limited to:
- 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 viceversa)?
- 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 much data is needed?
- 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?


13:20‑13:30 Introductory Remarks
13:30‑14:15 Ben Kuipers (UMich)
14:15‑14:30 Guglielmo Montone (Université Paris Descartes)
15:30‑16:00 Sergey Levine (UCB)
16:00‑16:10 Chelsea Finn (UCB)
16:10‑16:25 Johannes Stork (KTH)
16:25‑16:40 Martin Llofriu (USF)
16:40-16:55 Fabio Bonsignorio (SSSUP)
17:00‑17:30 Russ Salakhutdinov (U Toronto)

See workshop page for more details:


* Byron Boots (GATech) - bboots at cc.gatech.edu
* Andrea Censi (MIT) - censi at mit.edu

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

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