4 accepted papers
We introduce CRISP, a method that recovers simulatable human motion and scene geometry from monocular video. Prior work on joint human--scene reconstruction relies on data-driven priors and joint optimization with no physics in the loop, or recovers noisy geometry with artifacts that cause motion-tr…
Learning agile humanoid behaviors from human motion offers a powerful route to natural, coordinated control, but existing approaches face a persistent trade-off: reference-tracking policies are often brittle outside the demonstration dataset, while purely task-driven Reinforcement Learning (RL) can …
Humanoid robots hold the potential for unparalleled versatility by performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and real-world physics. Existing approaches, such a…
We present a new type of hydrostatic transmission that uses a hybrid air-water configuration, analogous to N+1 cable-tendon transmissions, using N hydraulic lines and 1 pneumatic line for a system with N degrees of freedom (DOFs). The common air-filled line preloads all DOFs in the system, allowing