Danfei Xu

SIDO enables static-trained policies to excel in dynamic environments, achieving higher success rates in moving object manipulation without sacrificing static performance.

The Temporal Ratio reveals how attention shifts between future and present frames can predict a model's ability to generalize compositional tasks in video-action contexts.

DINO and 3D motion flow can quadruple generalization capabilities for robots trained with egocentric human data, far surpassing traditional methods.

WARP achieves zero-shot whole-body mobile manipulation from offline human demonstrations, eliminating the reliance on teleoperation data.

Policies trained in SimFoundry's automated environments achieve up to 40% higher success rates in real-world tasks by leveraging affordance-preserving scene variations.

GRAFT enables robots to manipulate unseen objects with just one demonstration by leveraging geometric similarities, outperforming traditional semantic retrieval methods.

Robots can now learn to see and act simultaneously, achieving up to 34% better performance in occluded environments.

Extracting action signals from 32,041 hours of human video enables CAIP to outperform leading vision encoders in robotic manipulation tasks by over 30%.

Tactile-reactive policies can boost robotic manipulation success rates by over 30% through innovative data collection and a new Mixture-of-Transformers architecture.

EgoEngine transforms human manipulation videos into actionable robot demonstrations, enabling zero-shot learning without real-world data.

Real-time uncertainty quantification in 3D Gaussian Splatting is now possible, unlocking more robust active mapping and view synthesis.

Robots often ignore your commands mid-task, but ReSteer offers a way to fix this by pinpointing and patching the "blind spots" in their training data.