[robotics-worldwide] Reminder: CfP - RSS 2013 Workshop on "Hierarchical and Structured Learning for Robotics "

Gerhard Neumann neumann at ias.tu-darmstadt.de
Mon May 27 08:03:17 PDT 2013


RSS 2013 WORKSHOP ON "Hierarchical and Structured Learning for Robotics"


Title: Hierarchical and Structured Learning for Robotics

Gerhard Neumann (neumann at ias.tu-darmstadt.de, TU Darmstadt)
George Konidaris (gdk at csail.mit.edu, MIT Computer Science and Artificial 
Intelligence Laboratory)
Freek Stulp (freek.stulp at ensta-paristech.fr, ENSTA - ParisTech)
Jan Peters (peters at ias.tu-darmstadt.de, TU Darmstadt and MPI for 
Intelligent Systems)

WWW: http://www.ias.informatik.tu-darmstadt.de/Workshops/RSS2013

Learning robot control policies in complex real-world environments is a 
major challenge for machine learning due to the inherent high 
dimensionality, partial observability and the high costs of data 
generation. Treating robot learning as a monolithic machine problem and 
employing off-the-shelf approaches is unrealistic at best. However, the 
physical world can yield important insights into the inherent structure 
of control policies, state or action spaces and reward functions. For 
example, many robot motor tasks are also hierarchically structured 
decision tasks. For example, a tennis playing robot has to combine 
different striking movements sequentially. During locomotion there are 
at least three behaviors simultaneously active as a robot has to combine 
its gait generation with foot placement and balance control. First 
domain-driven skill learning approaches have already yielded impressive 
recent successes by incorporating such  structural insights into the 
learning process. Hence, a promising route to more scalable policy 
learning approaches includes the automatic exploitation of the 
environment's structure, resulting in new structured learning approaches 
for robot control.

Structured and hierarchical learning has been an important trend in 
machine learning in recent years. In robotics, researchers often ended 
up naturally at well-structured hierarchical policies based on 
discrete-continuous partitions (e.g., define local movement generators 
as well as a prioritized operational space control for combining them) 
with nested control loops at several different speeds (i.e., fast 
control loops for smooth and accurate movement achievement, slower loops 
for model-predictive planning). Furthermore, evidence from the fields 
cognitive sciences indicate that humans also heavily exploit such 
structures and hierarchies. Although such structures have been found in 
human motor control, are favored in robot control and exist in machine 
learning, the connections between these fields have not been well 
explored. Transferring insights from structured prediction methods, 
which make use of the inherent correlation in the data, to hierarchical 
robot skill learning may be a crucial step. General approaches for 
bringing structured policies, states, actions and rewards into robot 
reinforcement learning may well be the key to tackle many challenges of 
real-world robot environments and an important step to the vision of 
intelligent autonomous robots which can learn rich and versatile sets of 
motor skills. This workshop aims to reveal how complex motor skills 
typically exhibit structures that can be exploited for learning reward 
functions and to find structure in the state or action space.

In order to make progress towards the goal of structured learning for 
robot control, this workshop aims at researchers from different machine 
learning areas (such as reinforcement learning, structured prediction), 
robotics and related disciplines (e.g., control engineering, and the 
cognitive sciences).

We particularly want to focus on the following important topics for 
structured robot learning which have a big overlap from several of these 
     - Efficient representations and learning methods for hierarchical 
     - Learning in several layers of hierarchy
     - Structured representations for motor control and planning
     - Skill extraction and skill transfer
     - Sequencing and composition of behaviors
     - Hierarchical Bayesian Models for decision making and efficient 
transfer learning
     - Low-dimensional manifolds as structured representations for 
decision making
     - Exploiting correlations in the decision making process
     - Prioritized control policies in a multi-task reinforcement 
learning setup

These challenges are important steps to building intelligent autonomous 
robots and may potentially motivate new research topics in the related 
research fields.

The aim of this workshop is to bring together researchers which are 
in structured representations, reinforcement learning, hierarchical 
learning methods and control architectures.
Among these general topics we will focus on the following questions:

Structured representations:
     - How to efficiently use graphical models such as Markov random 
fields to exploit correlations in the decision making process?
     - How to extract the relevant structure (e.g. low dimensional 
manifolds, factorizations...) from the state and action space?
     - Can we efficiently model structure in the reward function or the 
system dynamics?
     - How to learn good features for the policy or the value function?
     - What can we learn from structured prediction?

Representations of behavior:
     - What are good representations for motor skills?
     - How can we efficiently reuse skills in new situations?
     - How can we extract movement skills and elemental movements from 
     - How can we compose skills to solve a combination of tasks?
     - How can we represent versatile motor skills?
     - How can we represent and exploit the correlations over time in 
the decision process?

Structured Control:
     - How to efficiently use structured representations for planning 
and control?
     - Can we learn task-priorities and use similar policies as in 
task-prioritized control?
     - How to decompose optimal control laws into elemental movements ?
     - How to use low-dimensional manifolds to control high-dimensional, 
redundant systems?
     - Can we use chain or tree-like structures as policy representation 
to mimic the kinematic structure of the

Hierarchical Learning Methods:
     - How can we efficiently apply abstractions to the control problem?
     - How to efficiently learn at several layers of hierarchy?
     - Which policy search algorithms are appropriate for which 
hierarchical representation?
     - Can we use hierarchical inverse reinforcement learning to acquire 
skill reward functions, and priors over selecting those skills?
     - How can we decide when to create new skills or re-use known ones?
     - How can we discover and generalize important sub-goals in our 
movement plan?

Skill Transfer:
     - How can we efficiently transfer skills to new situations?
     - Can we use hierarchical Bayesian models to learn in
       several layers of abstraction also in decision making?
     - How to transfer learned models or even value functions to new tasks?

June 1st - Deadline of submission for Posters
June 4th - Notification of Poster Acceptance

Extended abstracts (1 pages) will be reviewed by the program committee 
members on the basis of relevance, significance, and clarity. Accepted 
contributions will be presented as posters but particularly exciting 
work may be considered for talks. Submissions should be formatted 
according to the conference templates and submitted via email to 
neumann at ias.tu-darmstadt.de.

Gerhard Neumann, Technische Universitaet Darmstadt
George Konidaris, MIT Computer Science and Artificial Intelligence 
Freek Stulp, ENSTA - ParisTech
Jan Peters, Technische Universitaet Darmstadt and Max Planck Institute 
for Intelligent Systems

The most up-to-date information about the workshop can be found on the 
RSS 2013 webpage.

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