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?

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.