RoboPAD: Post-Training Adaptation of Robot Foundation Models

About the Workshop

Pretrained robot foundation models are increasingly capable, yet real-world deployment still requires adaptation to new environments, embodiments, tasks, and failures. RoboPAD focuses on post-training: how to adapt, correct, evaluate, and safely improve these models after pretraining.

We bring together researchers in robot learning, reinforcement learning, foundation models, world models, and embodied AI to consolidate post-training as a shared research agenda.

Call for Papers

We welcome work in progress, under-review work, and unpublished or preprint work substantially reframed as open-problem, benchmark, or perspective contributions. Archival published papers are not eligible.

Important Dates

Submission Tracks

Associated Challenge