Introduces a framework integrating vision-language-action models for whole-body humanoid locomotion, addressing optimal control and learning for complex bipedal behaviors. Combines VLA learning with locomotion-specific control for humanoid robots.

Proposes a motion-focused latent action representation for cross-embodiment vision-language-action policies learned from human videos, accepted to IROS 2026.

ManiFlow is a visuomotor imitation learning policy using consistency flow matching with a DiT-X architecture that generates high-quality actions in 1–2 steps. It works across single-arm, bimanual, and humanoid platforms using RGB or point cloud inputs.

RL-100 demonstrates highly reliable real-world RL manipulation achieving 900/900 success rates across 7 tasks with up to 250 consecutive trials without failure. It also shows strong robustness to disturbances and zero/few-shot adaptation capabilities.

A video-diffusion world model trained on over 1 million manipulation episodes (3,000 hours) that includes an action model and neural simulator for closed-loop robotic manipulation control, with all code and models open-sourced.

Proposes a hierarchical imitation learning framework using bilateral control, subtask-level progress tracking, and keyframe memory to handle long-horizon, contact-rich manipulation tasks.

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.