RoboPAD: Post-Training Adaptation of Robot Foundation Models

RoboPAD focuses on post-training adaptation of robot foundation models: the stage after large-scale pretraining where robots must be corrected, specialized, personalized, evaluated, and safely improved under real-world constraints. As vision-language-action models, diffusion and flow-matching policies, world models, and large-scale simulators become increasingly available, the next bottleneck is no longer only how to pretrain general robot policies, but how to adapt them to new environments, users, objects, embodiments, task constraints, and unexpected failures without restarting the full pretraining pipeline. The workshop will bring together researchers from robot learning, reinforcement learning, imitation learning, foundation models, world models, human-robot interaction, embodied AI, and safety. It will cover feedback- and intervention-driven adaptation, policy optimization after pretraining, world-model- and simulation-driven refinement, reasoning and memory for robot adaptation, cross-embodiment transfer, and evaluation, safety, and robustness after adaptation. A concrete outcome will be a public community roadmap summarizing shared terminology, evaluation protocols, benchmark gaps, and reporting practices.