Robot learning researchers
Attendees working on learned policies, representation learning, data-efficient adaptation, foundation models, and sim-to-real transfer for physical interaction.
Intended Audience
Locomotion, manipulation, and control communities
Researchers studying whole-body control, legged or humanoid robots, mobile manipulators, non-prehensile skills, and contact-rich motion.
Benchmark and deployment builders
Researchers designing tasks, datasets, simulation assets, evaluation protocols, safety metrics, and real-world testbeds for contact-rich loco-manipulation.
Dissemination
Accepted papers, slides, and the post-workshop open-problems report will be archived on this website when available.
Contact
Questions about participation, submissions, or logistics can be directed to the organizing team.