[robotics-worldwide] AURO Special Issue on Modern Approaches for Dexterous Manipulation

Heni Ben Amor amor at ias.informatik.tu-darmstadt.de
Wed Nov 7 04:50:11 PST 2012

Autonomous Robots Journal Special Issue:

Beyond Grasping - Modern Approaches for Dexterous Manipulation

The Autonomous Robots Journal invites papers for a special issue 
"Beyond Grasping: Modern Approaches for Dexterous Manipulation". In 
recent years,
grasping has matured to the point where various robots can reliably 
basic grasps on unknown objects in unstructured environments. While 
achievement is a major milestone for robotics, it has not yet 
translated into
major advances in manipulation. Instead, these robots are still far 
human-level manipulation. They still lack many manipulation
skills, ranging from sequenced multi-object tasks (such as stacking and 
tool usage)
to bimanual or in-hand manipulations of objects and interactions with 
non-rigid objects.
As these manipulations involve interacting with uncertain real-world 
they pose major problems for many current approaches and traditional 
methods that
depend on accurate models of the robot and its surrounding. Hence, 
there is strong
need for more advanced methods that can manipulate objects in the face 
of uncertainty.

Autonomous Robots seeks submissions Special Issue on “Beyond Grasping –
Modern Approaches for Dexterous 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 towards more 
advanced dextrous
manipulation skills. We invite submissions of research papers that 
important challenges in robot 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: January 15th, 2013
* First reviews completed: April 1st, 2013
* Revised papers due: April 30th, 2013
* Final decision: June 1st, 2013

Topics of interest include but are not limited to:

- What is the state-of-the-art in robot learning of manipulations?
- How can we benefit from recent results in machine learning, e.g.,
structured learning, Gaussian processes, conditional random fields,
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
- 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?
- What is the state of the art in robot hand technology?
- How much can we reliably learn from simulations?
- How can apprenticeship learning help to overcome the correspondence
- How can robots remove and place complex objects in cluttered
- 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
- 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 Jet Propulsion 
Ashutosh Saxena (asaxena at cs.cornell.edu) - Cornell University
Jan Peters (peters at ias.tu-darmstadt.de) - MPI for Intelligent 
Systems/TU Darmstadt

Papers must be prepared in accordance with the AURO guidelines.
All papers will be reviewed following the regular reviewing procedure 
the Journal.

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

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