Introduces a high-coverage, conformable robotic skin hardware system designed to improve data collection and policy learning for contact-rich, dexterous manipulation tasks. The system provides rich tactile sensing coverage to enable more capable robot manipulation policies.

A CAT-scan style diagnostic benchmark for robot foundation models that evaluates policies such as π0, π0.5, and Qwen-RobotManip beyond single success rates. The benchmark is designed to distinguish genuine generalization from overfitting to demonstrations in generalist manipulation policies.

A scalable VLA pretraining pipeline that converts unstructured egocentric human videos into robot training data, trains a dexterous hand VLA, and fine-tunes on robot data, achieving strong zero-shot generalization and real-robot dexterous manipulation.

A dexterous manipulation world model pretrained on 829 hours of EgoDex human data and DROID robot data using conditioned diffusion transformers, enabling open-loop rollouts and sim-to-real transfer with minimal robot fine-tuning.

Introduces PoLAR, a method that factorizes latent action representations into extent and mode components to improve robot policy learning efficiency and generalization.

Proposes a variational neural dynamics framework for continual robot policy learning, enabling robots to acquire new skills without forgetting previously learned ones.

Plug-and-play training framework that enforces physical plausibility in robotic video generation models, achieving SOTA on R-Bench, PAI-Bench, and EZS-Bench. Lifts WorldArena success rate from 16% to 24% with zero extra inference cost.

Replaces noisy 6DoF hand poses with relative wrist translation as a shared action space between cheap human videos and bimanual robots. Scales data-efficiently and outperforms full-pose baselines on manipulation tasks.

Pre-trains a dexterous hand via unstructured 'play' interactions with objects, then fine-tunes for precise assembly tasks including 0.5 mm clearance insertions and furniture screwing, achieving 33x better sample efficiency than RL from scratch.

First open-source Vision-Language-Action (VLA) model for dual-arm, dual-hand 36-DoF dexterous manipulation, trained on 100K simulated and 10K real trajectories with strong cross-embodiment transfer capabilities.