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 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. 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:
Following the success of the previous offering of the workshop at CoRL 2023, we propose a second iteration of the workshop at CoRL 2024. Specifically, the previous iteration of the workshop received a total of 26 submissions highlighting important characteristics and challenges of learning abstractions. They highlighted how pre-trained foundational models, specifically LLMs or VLMs, enable learning various forms of abstractions for a diverse set of robotics tasks. This iteration of the workshop would have a stronger emphasis on learning provably correct symbolic abstractions using different techniques.
We solicit papers of the following topics:
We solicit workshop paper submissions relevant to the above call of the following types:
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.
Paper Submission Deadline | September 22, 2024. AoE |
Paper Acceptance | October 16, 2024 |
Camera-ready Version Due | November 8, 2024. AoE |
Workshop | November 9, 2024 |