Uses reinforcement learning to improve process reasoning capabilities in robotic manipulation policies, shifting the model from passive observation to active critique.

ROVE leverages reinforcement learning to enable humanoid robots to benefit from human interventions during manipulation tasks.

A control framework for safe humanoid robot motion tracking using RL policies with real-time constraint enforcement via kinematics, dynamics, and control barrier functions.

Introduces spatio-temporal policy learning combined with physics-guided filtering to achieve robust and extremely agile robot control.

Introduces a tokenization-free autoregressive policy learning framework using flow matching across scales for robotic control.

Introduces a slow-fast imitation learning framework combining diffusion-based planning with reactive tactile/force feedback for contact-rich manipulation tasks. Also includes TactAR, an AR-based teleoperation system with tactile sensing.

Combines scalable autonomous reward modeling with RL-based refinement to improve vision-language-action policies on long-horizon dexterous manipulation tasks via autonomous rollouts. Demonstrates significant gains over imitation learning baselines.

Introduces coordination-aware structured action modeling for dual-arm robotic systems within a VLA framework. Addresses the unique challenges of bimanual manipulation through specialized action representations.

Proposes interleaved vision and language reasoning for robotic manipulation within a VLA framework. Aims to improve instruction following and task performance through integrated multimodal reasoning.

Self-directed play combined with Code-as-Policy for reusable skill acquisition and downstream manipulation tasks.