[robotics-worldwide] AURO Special Issue on Autonomous Grasping and Manipulation

Heni Ben Amor amor at ias.informatik.tu-darmstadt.de
Thu Feb 14 02:09:21 PST 2013


Autonomous Robots Journal Special Issue:

Autonomous Grasping and Manipulation

Paper submission deadline: March 1st, 2013

(Please Note: This is an updated version of the earlier CfP Beyond
Grasping - Modern Approaches for Dexterous Manipulation!! See also the
update of important dates.)

The Autonomous Robots Journal invites papers for a special issue
entitled "Autonomous Grasping and Manipulation". In recent years,
grasping has matured to the point where various robots can reliably
perform basic grasps on unknown objects in unstructured environments.
While this achievement is a major milestone for robotics, it still 
needs
to be scaled to unforeseen and more challenging situations, tasks as
well as manipulations. Current robots are still far from human-level
grasping and manipulation. Hence, there is strong need for more 
advanced
methods that can autonomously realize stable grasping and manipulation
of objects in the face of uncertainty.

Autonomous Robots seeks submissions Special Issue on “Autonomous
Grasping and Manipulation”. This special issue focuses on how modern
sensors data processing algorithms, movement generation approaches or
learning methods can help robots go beyond basic grasping abilities.
We invite submissions of research papers that address important
challenges in robot grasping and manipulation. We also solicit
submissions that rigorously discuss and compare current state of the 
art
techniques, as well as recent advances in the field, or open 
challenges.

Important Dates:
* Paper submission deadline: March 1st, 2013
* First reviews completed: April 15th, 2013
* Revised papers due: June 1st, 2013
* Final decision: July 1st, 2013

Topics of interest include but are not limited to:

- What is the state-of-the-art in autonomous robot grasping?
- What is the state of the art in robot hand technology?
- How can we build robot hands that facilitate grasping and
manipulation?
- How can we generalize efficient grasps to new, unseen objects?
- How can we benefit from recent results in machine learning, e.g.,
structured learning, Gaussian processes, conditional random fields,
deep belief networks?
- How can robots make use of reinforcement learning, or other
self-improvement methods, to adapt to changing environments and tasks?
- How can robots learn to handle ambiguous sensory signals?
- How can robots model uncertainty in their surroundings and their
actions?
- Which representations can leverage the acquisition of complete
multi-modal models of the environment?
- How can robots perform bimanual actions that are synchronized?
- How can robots determine optimal actions on non-rigid objects?
- How can robots learn to robustly detect the salient events in
manipulation tasks, e.g. when objects make and break contact?
- How much can we reliably learn from simulations?
- How can apprenticeship learning help to overcome the correspondence
problem?
- How can robots remove and place complex objects in cluttered
environments?
- How can we model finger synergies over longer action sequences?
- How can human task knowledge be efficiently transferred to robots?
- How can task-relevant features of objects be estimated?
- How can robots efficiently generalize a task from only a few human
demonstrations?
- How can a robot represent compound objects; e.g. objects stacked on
a tray or a bottle and a cap?
- How can the effects of actions be represented in a general form?
- What prior knowledge can a robot be expected to have?
- What are the key challenges and can we decide on benchmark tasks that
allow us to measure and compare progress in this field?
- Which datasets and code components can be shared, in order to allow
researchers to compare their respective methods and build upon each
other's work?

Guest Editors:
Heni Ben Amor (amor at ias.tu-darmstadt.de) - TU Darmstadt
Nicolas Hudson (Nicolas.H.Hudson at jpl.nasa.gov) - NASA JPL
Ashutosh Saxena (asaxena at cs.cornell.edu) - Cornell University
Jan Peters (peters at ias.tu-darmstadt.de) - MPI for Intelligent 
Systems/TU
Darmstadt

Submission:
Papers must be prepared in accordance with the AURO guidelines. Please 
read the following information on submissions:
http://www.springer.com/cda/content/document/cda_downloaddocument/AURO+CfP+-+Grasping.pdf?SGWID=0-0-45-1370402-0
http://www.springer.com/engineering/robotics/journal/10514
All papers will be reviewed following the regular reviewing procedure 
of
the Journal.

For more information, contact: amor at ias.tu-darmstadt.de


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