Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the grasp points and the unactuated interior. We present a hierarchical planning framework that combines a learned cloth model with a reduced-order whole-body model of a du …
In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization method generates object motions with minimal closed-loop sensitivity to uncertain parameters. The resulting reference trajectory is tracked using a coupled convex model pre …
Physical AI requires more than increasingly large robot datasets: intelligent robots acquire knowledge through continuous interaction with the physical world. We argue that the defining scientific resource of Physical AI is therefore not raw robot data alone, but robot experience - physically grounded interaction whose …