[Meetings] [CfP] CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP)

[David Paulius]

Greetings all, 

On behalf of my fellow organizing committee, I am pleased to share our call for submissions to the Learning Effective Abstractions for Planning (LEAP) workshop to be held at CoRL 2023.

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Workshop on Learning Effective Abstractions for Planning (LEAP)
Conference on Robot Learning (CoRL) | Atlanta, Georgia, USA | November 6-9, 2023
Website: https://leap-workshop.github.io/
Email: leap-w...@googlegroups.com
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Important Dates:

Paper submission deadline: September 18, 2023
Paper acceptance deadline: October 2, 2023

Aim and Scope of the Workshop:

Current research on abstraction learning for robotic planning is fragmented across several subcommunities including task and motion planning, reinforcement learning (hierarchical, model-based), planning with linear temporal logic, planning with natural language, and neuro-symbolic AI.

This workshop aims to create a common forum to share insights, discuss key questions, and chart a path forward for abstraction learning. For this to succeed, we will need to establish a shared understanding of what abstraction means in the context of robot planning. This will require bridge-building between different sub-disciplines, which we will facilitate in two ways: (1) a diverse selection of papers and invited speakers; and (2) a highly interactive workshop.

Key questions for discussion include:

1. What is the right objective for abstraction learning for robotic planning? To what extent should we consider soundness, completeness, planning efficiency, task distributions?
2. To what extent should the abstractions used for robotic planning be interpretable or explainable to a human? How can this be achieved?
   
3. When, where, and from what data should abstractions be learned? In the robot factory once and for all? In the “wild” from interaction with humans or the world?
   
4. How can and should pretrained language models (e.g., GPT-4) and vision-language models (e.g., CLIP) be leveraged towards abstraction learning for robotic planning?
   
5. To what extent is natural language a useful representation for abstraction learning? What are the virtues of alternative or additional representations?
   

Areas of Interest:

• Learning state abstractions and action abstractions
• Natural language as an abstraction for learning-based planning
• Learning other knowledge representations for planning
  
• Learning for LTL-based planning
  
• Learning for task and motion planning (TAMP)
  
• Learning for hierarchical planning
  
• Neuro-symbolic approaches for planning
  
• Hierarchical reinforcement learning
  
• Model-based reinforcement learning with latent representations
  
• Large Language Models/Foundation Models for Robot Planning

Submission Details:

We solicit workshop paper submissions relevant to the above call of the following types:

• Long papers -- up to 8 pages + unlimited references / appendices
• Short papers -- up to 4 pages + unlimited references / appendices

Please format submissions in CoRL or IEEE conference (ICRA or IROS) styles. To authors submitting papers rejected from other conferences: please ensure that comments given by the reviewers are addressed prior to submission.

Submissions will be accepted via OpenReview. Please note that a link to the submission page as well as further details and instructions are forthcoming!

Oral/Poster Presentation Details:

A selection of long papers will be invited for contributed talks. All accepted papers (long as well as short) and extended abstracts will be given a slot in the poster presentation session. Accepted submissions must be formatted in CoRL format for the camera-ready version.

Organizing Committee:

Georgia Chalvatzaki, Full Professor, TU Darmstadt, Germany
Beomjoon Kim, Assistant Professor, KAIST, South Korea
David Paulius, Postdoctoral Researcher, Brown University, RI, USA
Eric Rosen, Robotics Research Scientist, Boston Dynamics AI Research, MA, USA
Naman Shah, PhD Candidate, Arizona State University, AZ, USA
Tom Silver, PhD Candidate, Massachusetts Institute of Technology, MA, USA

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David
(On behalf of the Organizing Committee)