[Cfp] [Deadline extended] [Final Extension] CoRL 2024 Workshop on Learning Effective Abstractions for Planning (LEAP)

[Naman Shah]

(Apologies for cross-posting)

Greetings all,

Due to multiple requests, we have decided to extend the submission deadline to *Friday, October 11, 2024 AoE*!

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2nd Workshop on Learning Effective Abstractions for Planning (LEAP)

Conference on Robot Learning (CoRL) | Munich, Germany | November 9, 2024

Website: https://leap-workshop.github.io/

Email: leap-w...@googlegroups.com

Accepting Submissions via OpenReview 

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Important Dates:

Paper submission deadline: October 11, 2024

Paper acceptance notification: October 23, 2024

Workshop date: November 9, 2024

Aim and Scope of the Workshop:

There has been a renewed interest in using learning-based approaches to learn symbolic representations that support planning. However, this research is often fragmented in disjoint sub-communities such as task and motion planning, reinforcement learning (hierarchical, model-based), planning with formal logic, planning with natural language (language models), and neuro-symbolic AI.

Following the success of the 1st Workshop on Learning Effective Abstractions for Planning (LEAP) at CoRL 2023, this workshop aims to create a common forum to share insights, discuss key questions, and chart a path forward via abstraction.

Key questions for discussion include:

1. What is the right objective for abstraction learning for robotic planning? To what extent should we consider factors such as soundness, completeness, target planner and planning efficiency, and task distribution?
2. What level of abstraction is needed for it to be effective? How general-purpose or specific do these abstractions have to be for long-term autonomy? Do learned abstractions need to be hierarchical or at a single level?
3. To what extent should the abstractions used for robotic planning be interpretable or explainable to a human? How can this be achieved?
4. How can existing pre-trained foundational models (large language models (LLMs) and vision-language models (VLMs)) be utilized for learning symbolic abstractions while ensuring guarantees about correctness?
5. When, where, and from what data should abstractions be learned? Should they be learned as priors in the robot factory, using expert demonstrations, or in the “wild” from interaction with humans or the world?

Areas of Interest:

• Learning generalizable and composable representations for robot planning
  

• Learning for task and motion planning (TAMP)
• Learning state abstractions and action abstractions
• Natural language as an abstraction for learning-based planning
• Learning other knowledge representations for planning
• Learning for hierarchical planning
• Learning for planning with formal logic and methods (e.g., LTL)
• Neuro-symbolic approaches for task and motion planning
• Hierarchical reinforcement learning for robotics

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 must be submitted through OpenReview. 

Invited Speakers: 

• Georgia Chalvatzaki (TU Darmstadt)

• Caelan Garrett (NVIDIA)

• Leslie Kaelbling (MIT)

• Beomjoon Kim (KAIST)

• Eric Rosen (The AI Institute)

• Siddharth Srivastava (Arizona State University)

• Gregory Stein (George Mason University)

• Marc Toussaint (TU Berlin)

Organizing Committee:

Naman Shah, Postdoctoral Researcher, Brown University, RI, USA

David Paulius, Postdoctoral Researcher, Brown University, RI, USA

Nishanth Kumar, PhD Candidate, Massachusetts Institute of Technology, MA, USA

Jiayuan Mao, PhD Candidate, Massachusetts Institute of Technology, MA, USA

Yiqing Xu, PhD Candidate, National University of Singapore, Singapore

Nakul Gopalan, Assistant Professor, Arizona State University, AZ, USA

Rudolph Lioutikov, Assistant Professor, Karlsruhe Institute of Technology, Germany

George Konidaris, Associate Professor, Brown University, RI, USA


Best Regards, 
Naman Shah
Postdoc Researcher
Brown University