A unified RL policy for quadrupeds and humanoids that jointly handles force and position control without force sensors, enabling compliant behaviors, force-aware imitation learning, and contact-rich tasks.

Explores imitation learning approaches for multi-robot systems, focusing on policy coupling through sequential asymmetric imitation to enable collaborative robot behaviors.

A humanoid robot 'cerebellum' foundation model trained on 20,000 hours of human motion data that demonstrates scaling laws for robot motion control and enables zero-shot execution of unseen motions on real humanoids.

Combines the Spring-Loaded Inverted Pendulum (SLIP) model with reinforcement learning to achieve agile jumping behaviors in robotic systems.

A 9B model that filters noise from videos, GUI, and embodied data streams, reorganizing them into dense supervision via factual anchors and semantic synthesis; trained with SFT + GRPO across five domains with benchmarks.

Converts 6K+ hours of mixed human/robot egocentric video into robot pseudo-actions via camera-space alignment and reliability-aware loss, achieving 72.8% on RoboCasa and 91.1% on RoboTwin.

A 14B-parameter video world model that converts predicted visual futures into embodiment-agnostic actions via Jacobian inverse-dynamics, enabling zero-shot cross-robot transfer across a Panda arm and 16-DoF hand with open-sourced weights and training code.

A dexterous manipulation foundation model trained on 500k hours of real-world bimanual data that handles deformable objects such as cardboard folding and screw packing, featuring online retry and adaptation capabilities.

Develops an SE(3)-equivariant flow-based visuomotor policy leveraging spherical harmonics for efficient and geometrically consistent robot manipulation.

Introduces a dual-stream transformer architecture inspired by cortical visual processing for learning robotic manipulation policies.