Abstract:
As artificial intelligence advances quickly in the digital domain, the next
frontier lies in physical intelligence: systems that learn through acting and
sensing in the real world. In this thesis, we explore practical ways of scaling
such embodied data across three directions. AnyCar scales synthetic data
through large-scale simulation, training a universal dynamics transformer
that generalizes across vehicles and environments. FACTR improves
the efficiency of real robot data with a low-cost bilateral teleoperation
system and a curriculum that teaches policies to integrate force and
vision. DexWild scales human data through in-the-wild data collection
and co-training with robot demonstrations, enabling generalization to
unseen objects and environments. Together, these projects explore how a
data-centric approach can enable more adaptive and capable robots.

Committee:
Deepak Pathak (chair)
Guanya Shi
Kenneth Shaw