Real2Sim2Real presents a promising opportunity for zero-shot robot deployment π€, but how does this paradigm translate to tricky, contact-rich applications such as the game of billiards π±?
We argue that an agent can learn the underlying dynamics parameters through interaction!
Can an agent learn a pool tableβs physics by playing on it? π±
While agents can close the visual gap, the difference between real world and sim dynamics remains significant. For CoRL late breakingπ€ , we let an agent take real shots to try and close this real2sim dynamics gap: https://t.co/g12jkuuB4n
Very fun short project with @XilunZhangXZ@MatthewFoutter@Luis_A_Pabon@drmapavone
β¨ Test-Time Scaling for Robotics β¨
Excited to release π€ RoboMonkey, which characterizes test-time scaling laws for Vision-Language-Action (VLA) models and introduces a framework that significantly improves the generalization and robustness of VLAs!
π§΅(1 / N)
π Website: https://t.co/pYvytO8OmZ
π» Code: https://t.co/bDhjWoNBim
ποΈ Datasets and Models: https://t.co/u97uiAFk9V
π Serving Engine: https://t.co/JIuhS9xgUw
π Paper: https://t.co/q59LkD2iSV