2 papers
cs.RO2025
Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
Ben Moran, Mauro Comi, Arunkumar Byravan +4
Creating accurate, physical simulations directly from real-world robot motion holds great value for safe, scalable, and affordable robot learning, yet remains exceptionally challen…
cs.RO2025
Proc4Gem: Foundation models for physical agency through procedural generation
Yixin Lin, Jan Humplik, Sandy H. Huang +18
In robot learning, it is common to either ignore the environment semantics, focusing on tasks like whole-body control which only require reasoning about robot-environment contacts,…