Diffusion for Robot Learning

Vision

Diffusion has established itself as a dominant learning and generation paradigm, emerging as a powerful alternative to autoregressive schemes. While its impact on image and video synthesis is well-established, its potential to reshape robot learning is vast. We believe the robot learning community is currently entering a more embracing phase for diffusion methods, where they serve as a foundation for both high-fidelity data generation and intelligent control.

This workshop aims to aggregate recent advances in diffusion modeling into trackable robotics capabilities and pinpoint the most promising future directions for the community, standing at the intersection of generative AI and physical intelligence.

Topics of Interest

Diffusion as a Policy

Data-efficient and scalable training for generalizable policies.

Steering Policies

Gradient-based and classifier-free guidance for goal-reaching.

Diffusion for VLA

Integrating diffusion into Vision-Language-Action models.

Dynamics of Denoising

Analyzing the iterative action formation process.

Accelerating Inference

Few-step diffusion, Consistency Models, and Flow Matching.

World Models

Semantic physics knowledge from large video models.

Motion Synthesis

Diffusion-based generation of high-fidelity human/hand poses to solve data scarcity.

Invited Speakers

C. Karen Liu

Stanford University

Yilun Du

Harvard University

Max Simchowitz

CMU

Li Yi

Tsinghua University

Ian Manchester

University of Sydney

Workshop Schedule (Tentative)

Time	Event	Speaker(s)
08:00 - 08:10	Opening Remarks	Organizers
08:10 - 09:00	Keynote 1-2, Diffusion as Policy	TBD
09:00 - 09:30	Coffee Break & Poster Session	-
09:30 - 10:20	Keynote 3-4, Diffusion for VLA	TBD
10:20 - 10:50	Spotlight Talks	-
10:50 - 11:40	Keynote 5-6, Diffusion as World Model	TBD
11:40 - 12:00	Sponsor Talk & Closing Remarks & Awards	Organizers

Call for Papers

We welcome submissions that explore the intersection of generative modeling and physical intelligence, ranging from theoretical foundations to large-scale robotic deployments.

Submission Guidelines

Paper Length: 4 to 8 pages (excluding references and supplementary material).
Format: Submissions must follow the RSS paper template.
Review Process: Peer review will be single-blind.
Dual Submissions: As a non-archival venue, we welcome submissions of work that is currently under review or has been recently accepted/published at other venues.

Presentation & Awards

All accepted papers will be presented during an on-site poster session. A subset of high-quality submissions will be selected for 5-min spotlight talks. During the closing remarks, we will announce the Best Paper Award. Accepted papers will also be made available on the workshop website.