Learning, Perception, and Abstraction for Long-Horizon Planning

CoRL 2022 Workshop, Dec 15


Even as robots have become more capable at solving manipulation and planning tasks, most solution strategies are limited to problems that are quite short or narrow in scope by human standards. Abstraction—the ability for an agent to reason at a higher level about salient aspects of a task—has long been a tool for roboticists to mitigate the challenges of imperfect perception and long-horizon reasoning associated with planning. Yet useful abstractions are difficult to come by in a world rife with complexity; for many real-world problems that require planning far into the future, it is often unclear how to abstract knowledge and perception without degrading performance or discarding information the agent may need to effectively complete its task.

Rather than hand-designing representations for state, action, and perceptual abstraction, there has been significant recent interest in learning part or all of these representations—e.g., via reinforcement learning, direct supervision, or informed via natural language—in an effort to mitigate the computational challenges of planning without compromising performance. Despite the enormous potential of this direction, robot planning has yet to have its ImageNet moment. In particular, it is as of yet unclear the role that learning will play in such a future: how much should be learned or hand-designed in this context?

This workshop is aimed at bringing together researchers at the intersection of planning and learning, both for perception and action, in an effort to systematize the discussion of long-horizon planning and the abstractions that enable it. In particular, participants will seek to better understand the challenges that limit progress in this space and the relative potential of different strategies to overcome these challenges. Moreover, we adopt an inclusive (rather than exclusive) definition of what constitutes long-horizon in an effort to welcome all researchers who seek to develop the capabilities necessary to “scale up” planning in their application domain. We welcome submissions including, but not limited to, the following topics:

Some Motivating Questions


All times local, New Zealand Daylight Time [UTC+13]

Invited Speakers

Angel Chang
Simon Fraser University
perception, language, embodied agent navigation
Jitendra Malik
UC Berkley
perception, embodied agents
Roozbeh Mottaghi
FAIR & University of Washington
perception, embodied agents, rearrangements
Kiana Ehsani
Allen Institute for Artificial Intelligence
embodied agents, mobile manipulation
George Konidaris
Brown University
RL, hierarchies, skills to symbols
Subbarao Kambhampati
Arizona State University
planning, information extraction
Dylan Hadfield-Menell
Massachusetts Institute of Technology
learning for task and motion planning
Lerrel Pinto
New York University
RL, large-scale robot learning


We invite two different types of submissions:

Short Papers (for poster presentation) in standard CoRL paper format for a maximum of 4 pages; submissions for this workshop need not be anonymized. We welcome research from broad areas at the intersection of robot planning, perception, and machine learning. Accepted papers will be featured at poster sessions and featured on the workshop website.

Early-Career "Blue Sky" Papers (particularly from early-career academics) in standard CoRL paper format; though submissions for this workshop need not be anonymized. We seek "Blue Sky" submissions, 2-4 pages in length, that present a novel high-level perspective of the challenges associated with long-horizon planning and relevant solution strategies. Four such submissions will be selected for 10-15 minute talks and a panel discussion with questions from the audience and authors featured on the workshop website. Preference will be given to early career academics—senior graduate students, postdocs, and pre-tenure faculty.

Papers may be submitted in PDF format via email to the address: [FORTHCOMING].

We encourage submissions from early-career academics, women, minorities, and members of other underrepresented groups.


Gregory J. Stein
George Mason University
Rohan Chitnis
Massachusetts Institute of Technology
‘YZ’ Yezhou Yang
Arizona State University
Tom Silver
Massachusetts Institute of Technology
Jana Košecká
George Mason University