[robotics-worldwide] [journals] CfP: IJRR Special Issue on Task and Motion Planning, Deadline Aug. 1

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[robotics-worldwide] [journals] CfP: IJRR Special Issue on Task and Motion Planning, Deadline Aug. 1

Neil T. Dantam-2
Call for Papers

It is our pleasure to announce the International Journal of Robotics
Research Special Issue on Task and Motion Planning.  We invite all
interested researchers to submit papers for this special issue.

Important Info

* Initial Submission: August 1, 2018
* First Review: December 2018
* Revised Submission: February 2019
* Target Publication: Summer 2019

Submission site: https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_ijrr&d=DwICaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Wlq0X6VfIMXfdmfrs4UcHfJ1Bt31xXh97YKDkIOjHzg&s=1VmDdkmeNwYJ4rPvqmTkSjnuMo8SGKq7EfwRh0_UbXI&e=

Under "Manuscript Type," please choose "Task and Motion Planning."

Special Issue Objectives

The integration of task planning over high-level actions with motion
planning over continuous paths enables robots to autonomously achieve
complex objectives in complex environments.  Recent years have seen
numerous developments in Task and Motion Planning (TMP), drawing
inspiration from artificial intelligence, formal methods, and the
traditional robotics community.  TMP reasons over a rich space of
states and actions, enabling robots to compose motions, grasps, or
other capabilities to achieve their goals.  Efficient frameworks for
TMP are critically important for manipulation, service robotics, and
field robotics.  Moreover, TMP provides theoretical foundations for
the study of autonomy.  Previous research in TMP has focused both on
algorithmic properties, such as completeness or optimality, and on
applications in a wide range of scenarios. This special issue seeks to
bridge the gaps in focus and assumptions, contextualize the varying
TMP methods, and promote new advances in integrated reasoning.

Topics of interest include:

* Algorithms for TMP
* Feedback between task and motion planners
* Representations and complexity
* Uncertainty in task and motion systems
* Task or motion plan caching and re-use
* Execution and control of task and motion plans
* TMP with (human-)robot teams
* Handling uncontrollable agents / Reactive synthesis
* Learning-based approaches for TMP and related problems

Guest Editors

* Neil T. Dantam, Colorado School of Mines
* Swarat Chaudhuri, Rice University
* Lydia E. Kavraki, Rice University
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