Connectionists: Probabilistic Approaches for Robotics and Control (NIPS workshop) - 2nd call for contributions

[Marc Deisenroth]

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                  CALL FOR CONTRIBUTIONS

                      NIPS workshop on
       *Probabilistic Approaches for Robotics and Control*
                  (supported by PASCAL 2)
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*Workshop dates*
Friday, December 11, 2009

*Workshop location*
Whistler, B.C., Canada, at the Westin Resort and Spa and the
Hilton Whistler Resort and Spa


*Poster submission*

Please send an extended abstract of max. 1 page describing the
poster you intend to present to
mpd37 at cam.ac.uk
Choose a format of your liking, e.g., the standard NIPS
template.

The *deadline for abstract submissions* is
October 17, 2009.

The *notification* will be
October 26, 2009.

*Workshop homepage*
http://mlg.eng.cam.ac.uk/marc/nipsWS09


*Conference homepage*
http://nips.cc


*Workshop Abstract*

During the last decade, many areas of Bayesian machine learning have
reached a high level of maturity. This has resulted in a variety of
theoretically sound and efficient algorithms for learning and
inference in the presence of uncertainty. However, in the context of
control, robotics, and reinforcement learning, uncertainty has not yet
been treated with comparable rigor despite its central role in
risk-sensitive control, sensorimotor control, robust control, and
cautious control. A consistent treatment of uncertainty is also
essential when dealing with stochastic policies, incomplete state
information, and exploration strategies.

A typical situation where uncertainty comes into play is when the
exact state transition dynamics are unknown and only limited or no
expert knowledge is available and/or affordable. One option is to
learn a model from data. However, if the model is too far off, this
approach can result in arbitrarily bad solutions. This model bias can
be sidestepped by the use of flexible model-free methods. The
disadvantage of model-free methods is that they do not generalize and
often make less efficient use of data. Therefore, they often need more
trials than feasible to solve a problem on a real-world system. A
probabilistic model could be used for efficient use of data while
alleviating model bias by explicitly representing and incorporating
uncertainty.

The use of probabilistic approaches requires (approximate) inference
algorithms, where Bayesian machine learning can come into
play. Although probabilistic modeling and inference conceptually fit
into this context, they are not widespread in robotics, control, and
reinforcement learning. Hence, this workshop aims to bring researchers
together to discuss the need, the theoretical properties, and the
practical implications of probabilistic methods in control, robotics,
and reinforcement learning.

One particular focus will be on probabilistic reinforcement learning
approaches that profit recent developments in optimal control which
show that the problem can be substantially simplified if certain
structure is imposed. The simplifications include linearity of the
(Hamilton-Jacobi) Bellman equation. The duality with Bayesian
estimation allow for analytical computation of the optimal control
laws and closed form expressions of the optimal value functions.
Format

The workshop will consist of short invited presentations and a session
with contributed posters (plus poster spotlight). Topics (from a
theoretical and practical perspective) to be addressed include, but
are not limited to:

- How can we efficiently plan and act in the presence of uncertainty
   in states/rewards/observations/environment?

- Shall we model the lack of knowledge or can we simply ignore it?

- How can prior knowledge (e.g., expert knowledge and domain
   knowledge) be incorporated?

- How much manual tuning and human insight (e.g., domain
   knowledge) is a) required and b) available to achieve good
   performance?

- Is there a principled way to account for imprecise models and
   model bias?

- What roles should probabilistic models play in control? Are they
   needed at all?

- What kinds of probabilistic models are useful?

- In traditional control, hand-crafted control laws often prevail
   since optimal control laws are mostly too aggressive due to
   model errors while robust control laws can be too conservative
   since they always assume the worst case. Can "probabilistic
   control" bridge the gap between robust and optimal control laws?

- How can we exploit the linearity of the (Hamilton-Jacobi) Bellman
   equation and the duality with Bayesian estimation?

- Can we compute the optimal control law analytically and is there a
   closed-form expression of the value function?

- How can existing machine learning methods be applied to efficiently
   solve stochastic control problems?


*Invited speakers*

Dieter Fox (University of Washington), confirmed
Drew Bagnell (CMU), pending
Evangelos Theodorou (USC), confirmed
Jovan Popovic (MIT), confirmed
Konrad Koerding (Northwestern University), confirmed
Marc Toussaint (TU Berlin), confirmed
Miroslav Karny (Academy of Sciences of the Czech Republic), confirmed
Roderick Murray-Smith (University of Glasgow), confirmed
Bert Kappen (University of Nijmegen), confirmed
Emanuel Todorov (University of Washington), confirmed


*Support*
The workshop is supported by PASCAL2 and the Technical Committee on 
Robot Learning


*Organizers*

Marc Peter Deisenroth
Bert Kappen
Emanuel Todorov
Duy Nguyen-Tuong
Carl Edward Rasmussen
Jan Peters