LEAP: Learning Effective Abstractions for Planning

Overview

Complex long-horizon and sparse reward robotics tasks are challenging for scaling end-to-end learning methods. By contrast, planning approaches have shown great potential to handle such complex tasks effectively. One of the major criticisms of planning-based approaches has been the lack of availability of accurate world models (aka abstractions) to utilize.

There has been a renewed interest in using learning-based approaches to learn symbolic representations that support planning. However, this research is often fragmented into 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. This workshop aims to create a common forum to share insights, discuss key questions, and chart a path forward via abstraction.

We aim to facilitate this bridge-building in two ways: (1) a diverse selection of papers and invited speakers; and (2) a highly interactive workshop. Concretely, the workshop will highlight approaches that use different learning methods, mainly to learn symbolic and composable representations of world models. Key questions for discussion include:

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?
How can abstraction-based systems in the role of data collectors facilitate contemporary learning methods such as imitation learning? Can we develop insights into what can be explicit and implicit priors that would allow efficient long-horizon task learning, and can we derive them from abstraction-based planning systems?
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?
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 soundness and correctness? How can we also incorporate cost and plan quality? How can we incorporate cost-sensitive reasoning into vision-language and large model-based abstractions to produce efficient and executable plans for embodied agents?
How can learned abstractions enable safer and decidable outcomes for robot skills learned in the form of robot action foundation models such as OpenVLA, Pi, and LBMs? Can we learn symbolic models for such action foundation models that enable off-the-shelf planners to be used in open-world settings?
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? What are the trade-offs between top-down operator construction (e.g., via symbolic abstraction or language) and bottom-up operator discovery (e.g., through exploration or policy learning), and how do they affect generalization and planning efficiency?

Following the success of the previous offerings of the workshop at CoRL 2023 and CoRL 2024, we propose a third iteration of the workshop at CoRL 2025. Specifically, the previous iterations of the workshop received a total of 26 and 55 submissions, respectively. These submissions highlighted important characteristics and challenges of learning abstractions while showcasing the abstraction capabilities of pre-trained LLMs and VLMs. This iteration of the workshop would have a stronger emphasis on works that highlight the capabilities of using high-dimensional data and pre-trained models for learning sound and complete abstractions that enable cost-effective and reliable planning.

Areas of Interest

We solicit papers of the following topics:

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 LTL-based planning
Neuro-symbolic approaches for task and motion planning
Hierarchical reinforcement learning for robotics

Submission Guidelines

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

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

Please format submissions in CoRL or IEEE conference (ICRA or IROS) styles. Submissions do not need to be anonymized. To authors submitting papers rejected from other conferences: please ensure that comments given by the reviewers are addressed prior to submission.

Note: Please feel free to submit work under review or accepted for presentation at other workshops and/or conferences as we will not require copyright transfer.

We are now accepting submissions through our OpenReview portal

Important Dates

Paper Submission Deadline	Aug 12, 2025 (Early)
Paper Submission Deadline	Sep 5, 2025 (Late)
Author Notification	Aug 27, 2025
Camera-ready Version Due	Sept 22, 2025
Workshop	Sept 27, 2025

We offer two submission deadlines to accommodate different planning needs: an early deadline and a late deadline. The early deadline is intended for authors who may require quicker notification for travel, visa, or funding arrangements. Submissions received by the early deadline will be reviewed promptly, and notifications will be sent within approximately two weeks of submission. Authors who do not require early feedback are welcome to submit by the late deadline.