Despite significant recent advances, planning remains a fundamentally hard problem, especially when considering robotic applications with long-horizon tasks, sparse feedback, and continuous state and action spaces. Abstraction is one of the main tools we have to overcome such challenges. State abstraction allows agents to focus on the important aspects of a planning problem, and action abstraction allows agents to reason across long horizons and exploit hierarchy in task execution. Designing abstractions by
This workshop will bring together researchers from several related but often disjoint subcommunities who share an interest in learning abstractions for robotic planning. Key questions for discussion include: What is the right objective for abstraction learning for robotic planning? To what extent should we consider soundness, completeness, planning efficiency, task distributions? To what extent should the abstractions used for robotic planning be interpretable or explainable to a human? How can this be achi
This workshop comes at a pivotal time as the field works to understand the implications of large pretrained language and vision models for robotic planning. As suggested by the latter discussion questions, these foundation models will be a central workshop theme. We believe there are rich opportunities not only for foundation models to aid robotic planning, but also for robotic planning research to inform the further development of foundation models. For example, the “right objective” for abstraction learni
Utkarsh A. Mishra
Hejia Zhang
Home | Nishanth J. Kumar
Nakul Gopalan
Wenlong Huang
Yunshuang Li
Utkarsh A. Mishra
Eric Rosen's Website
Tabitha Edith Lee ~ Roboticist
Jason Ma
Hejia Zhang