[agents] [meetings] Call for Participation - PlanRob Workshop on Planning and Robotics - International Conference on Automated Planning and Scheduling - ICAPS 2022

[AndreA Orlandini]

Apologies for multiple copies

We are glad to invite you to attend the 10th ICAPS workshop on Planning 
and Robotics (PlanRob 2022).

The event will be on the 16th of June as an online virtual event and the 
program is full of interesting papers.
The workshop program is also enriched by a keynote talk of Maxim 
Likhachev (Carnegie Mellon University) on "Learning in Search-based 
Planning for Robotics"
(see below for details).

You may find more details about the program on the workshop web page and 
just below.

Thanks for your attention

Best,
Iman Awaad, Alberto Finzi, AndreA Orlandini
PlanRob 2022 Chairs


                          ** CALL FOR PARTICIPATION **

                 10th ICAPS Workshop on Planning and Robotics
                                     (PlanRob‚ 2022)

http://icaps22.icaps-conference.org/workshops/PlanRob/

                                ICAPS 2022 Workshop
                               (Virtually in) Singapore
                                     June 16 20222
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Program   (Schedule in UTC)
***************

>From 	To 	Title 	Authors
12.00 	12.10 	PlanRob Intro 	Iman Awaad, Alberto Finzi, AndreA Orlandini
12.10 	12.30 	Time-Bounded Large-Scale Mission Planning Under 
Uncertainty for UV Disinfection 	Lara Brudermüller, Raunak 
Bhattacharyya, Bruno Lacerda and Nick Hawes
12.30 	12.50 	Probabilistic Planning for AUV Data Harvesting from Smart 
Underwater Sensor Networks 	Matthew Budd, Georgios Salavasidis, Izzat 
Kamarudzaman, Catherine Harris, Alexander Phillips, Paul Duckworth, Nick 
Hawes and Bruno Lacerda.
12.50 	13.10 	Towards Using Promises for Multi-Agent Cooperation in Goal 
Reasoning 	Daniel Swoboda, Till Hofmann, Tarik Viehmann and Gerhard 
Lakemeyer.
13.10 	13.30 	Improving Task Planning Knowledge Robustness for 
Autonomous Robots 	Hadeel Jazzaa, Thomas McCluskey and David Peebles.
13.30 	13.50 	Towards an Easy Use Case Implementation in Social Robotics 
	Alba Gragera, Carmen Díaz de Mera, Alberto Tudela, Alejandro Cruces, 
Fernando Fernández and Ángel García-Olaya.
13.50 	14.10 	Coffee Break 	
14.10 	15.10 	Learning in Search-based Planning for Robotics 	Maxim 
Likhachev <http://www.cs.cmu.edu/~maxim/>
15.10 	15.30 	Understanding Natural Language in Context 	Avichai Levy 
and Erez Karpas.
15.30 	15.50 	Coffee Break 	
15.50 	16.10 	Analysis and Utilisation of Conflicts in Multi-Agent Path 
Finding 	Avgi Kollakidou and Leon Bodenhagen.
16.10 	16.30 	Learning Path Constraints for UAV Autonomous Navigation 
under Uncertain GNSS Availability 	Marion Zaninotti, Charles Lesire, 
Yoko Watanabe and Caroline P. Carvalho Chanel.
16.30 	16.50 	Conflict-Based Multi-Robot Multi-Goal Task and Motion 
Planning 	Junho Lee and Derek Long.
16.50 	17.10 	Towards Adversarial Geometric Planning 	Stefan Edelkamp
17.10 	17.30 	Asynchronous Motion Planning and Execution for a Dual-Arm 
Robot 	Charles Meehan, Mark Roberts and Laura Hiatt
17.30 	18.00 	Panel Discussion





**


          Keynote Talk byMaximi Likhachev (http://www.cs.cmu.edu/~maxim/)



          Title: Learning in Search-based Planning for Robotics


          Abstract: Search-based Planning refers to planning by
          constructing a graph from systematic discretization of the
          state- and action-space of a robot and then employing a
          heuristic search to find a path from the start to the goal
          vertex in this graph. In this talk, I will first quickly
          overview strengths and challenges of this paradigm and briefly
          mention some of the work my group has done towards addressing
          these challenges. I will then focus on some of the work my
          group has recently done on integrating learning into
          Search-based Planning. In particular, I will present a) a
          novel class of planning algorithms called Constant-time Motion
          Planning (CTMP) that use offline preprocessing to ensure that
          online planning can provably guarantee to return solutions
          within a (small) constant time for repeated planning tasks,
          and b) our work towards planning that provides strong
          guarantees on achieving a task despite using inaccurate models
          for planning. Finally, I will conclude with my thoughts on
          outstanding challenges, in particular as related to
          integrating learning and planning in the context of robotics.




-- 
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AndreA Orlandini PhD

  National Research Council of Italy
  Institute for Cognitive Science and Technology
  Phone:  +39-06-44595-223      E-mail: andrea.orlandini at istc.cnr.it
  Fax:    +39-06-44595-243      Url: http://www.istc.cnr.it/group/pst
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Me, the one and only person that never leaves me alone!

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