Yao Mu

Current vision-language models struggle with process understanding in robotic manipulation, but targeted post-training can yield significant improvements.

Achieving 86.4% grasp stability and 83.3% real-world success, SynManDex bridges the gap between human dexterity and robotic manipulation.

AHA-WAM achieves a remarkable 92.80% success rate on RoboTwin while executing actions at 24.17 Hz, all without the need for prior robot-data training.

IDP achieves high-frequency robot control by enforcing action manifold constraints without the computational burden of iterative sampling.

Human-in-the-loop chunk-wise residual adaptation closes the reality gap for dexterous robot manipulation, boosting success rates by up to 43% compared to offline imitation learning.

Decoupling high-level VLM planning from low-level diffusion-based control lets robots reason like foundation models *and* execute precisely, outperforming end-to-end approaches in complex manipulation tasks.

Forget painstakingly creating 3D assets for robot training - ManiTwin automates the process, turning single images into simulation-ready objects at scale.