Introduces a real-time 3D-aware policy framework for embodied manipulation tasks, enabling spatially grounded action generation.

Presents a vision-language-action model grounded in 3D Gaussian representations that integrates geometric and semantic awareness for improved robotic manipulation.

Leverages symmetric Koopman operator predictions within a reinforcement learning framework to achieve faster training and better generalization for legged robot locomotion.

A tactile foundation model and sim-to-real pipeline designed for dexterous manipulation tasks. Combines transformer-based tactile sensing with a sim-to-real transfer approach.

A tactile-informed whole-body loco-manipulation controller for quadrupedal robots that unifies locomotion and manipulation using tactile sensing. Enables compliant and contact-aware whole-body control.

A contact-centric whole-arm manipulation framework that conditions control on both tactile and visual inputs. Targets dexterous manipulation tasks requiring rich contact feedback.

SIEVE introduces structure-aware data selection specifically for imitation learning with Vision-Language-Action models, aiming to improve training efficiency and policy quality.

An RL algorithm that learns long-horizon bimanual dexterous policies for any robot hand from a single human demonstration, emphasizing generalization across hands, objects, and complex motions.

A unified platform comprising GE-Base (video diffusion model trained on 1M+ manipulation episodes), GE-Act (flow-matching action model), and GE-Sim (neural world simulator for closed-loop control). All code, models, and benchmarks are open-sourced.

Introduces test-time training (TTT) inside the policy to natively scale visuomotor context to 8K timesteps at constant inference cost, enabling one-shot imitation from human video demos, online self-recovery from errors, and long-horizon assembly tasks.