Unifies VLA and world-modeling in a single autoregressive transformer that predicts both future images and actions. Outperforms separate VLA or world models on LIBERO simulation benchmarks.

HumDex targets humanoid dexterous manipulation, aiming to simplify the development of dexterous manipulation capabilities for humanoid robots.

Presents an open-source, low-cost sEMG wristband framework that extracts force signals from human muscle activity in videos, enabling zero-shot human-to-robot transfer of forceful manipulation policies across any robot, camera, or environment.

Introduces ICWM, a method that learns world dynamics from just seconds of a robot's self-generated interaction data, enabling zero-shot adaptation to unseen cameras and new robot morphologies without any fine-tuning.

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.