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Bridging the gap between human and robotic manipulation, HandEdit enables scalable learning for dexterous robotics using abundant egocentric video data.
Human demonstrations can yield over 10x the recovery data for robots, dramatically enhancing their ability to recover from failures in real-world tasks.
Generating visually faithful driving simulations just got a boost with a novel framework that stabilizes error accumulation and enhances realism in closed-loop scenarios.
Role-aware training can boost video diffusion models' physical consistency by up to 39.4% without sacrificing visual fidelity.
UniDexTok slashes reconstruction errors by over 98% for dexterous hands, achieving unprecedented accuracy without relying on retargeting.
Closed-loop evaluation reveals how VLMs for autonomous driving handle the messy reality of off-road deviations and out-of-distribution states, something static QA datasets can't capture.
Spatial reasoning could be the secret sauce for building generalist embodied agents that can drive, manipulate objects, and fly drones, all within a single model.
