Contact-Aware Neural Dynamics

High-fidelity physics simulation is essential for scalable robotic learning, but the sim-to-real gap persists, especially for tasks involving complex, dynamic, and discontinuous interactions like physical contacts. Explicit system identification, which tunes explicit simulator parameters, is often insufficient to align the intricate, high-dimensional, and state-dependent dynamics of the real world. To overcome this, we propose an implicit sim-to-real alignment framework that learns to directly align the simulator's dynamics with contact information. Our method treats the off-the-shelf simulator as a base prior and learns a contact-aware neural dynamics model to refine simulated states using real-world observations. We show that using tactile contact information from robotic hands can effectively model the non-smooth discontinuities inherent in contact-rich tasks, resulting in a neural dynamics model grounded by real-world data. We demonstrate that this learned forward dynamics model improves state prediction accuracy and can be effectively used to predict policy performance and refine policies trained purely in standard simulators, offering a scalable, data-driven approach to sim-to-real alignment.

StageACT: Stage-Conditioned Imitation for Robust Humanoid Door Opening

Humanoid robots promise to operate in everyday human environments without requiring modifications to the surroundings. Among the many skills needed, opening doors is essential, as doors are the most common gateways in built spaces and often limit where a robot can go. Door opening, however, poses unique challenges as it is a long-horizon task under partial observability, such as reasoning about the door's unobservable latch state that dictates whether the robot should rotate the handle or push the door. This ambiguity makes standard behavior cloning prone to mode collapse, yielding blended or out-of-sequence actions. We introduce StageACT, a stage-conditioned imitation learning framework that augments low-level policies with task-stage inputs. This effective addition increases robustness to partial observability, leading to higher success rates and shorter completion times. On a humanoid operating in a real-world office environment, StageACT achieves a 55% success rate on previously unseen doors, more than doubling the best baseline. Moreover, our method supports intentional behavior guidance through stage prompting, enabling recovery behaviors. These results highlight stage conditioning as a lightweight yet powerful mechanism for long-horizon humanoid loco-manipulation.