Ruoqu Chen

A framework that learns visual-tactile dexterous manipulation skills from human videos by leveraging simulated interaction, bridging the gap between passive human demonstrations and executable robot policies.

Uni-VLaT adds distributed whole-body touch to pretrained vision-language-action policies. Its tactile features support action generation and predict future tactile, proprioceptive, and visual representations, improving contact-rich humanoid control across five real-robot tasks with a 75% average success rate.

Present UltraDP, a Diffusion-Policy-based method that receives multi-sensory inputs (ultrasound images, wrist camera image, contact wrench, and probe pose) and generates actions that are fit for multi-modal action distributions in autonomous ultrasound scanning. UltraDP achieves a 95% success rate in transverse scanning on previously unseen subjects, demonstrating its effectiveness.

WEAVE learns coordinated humanoid locomotion and dexterous manipulation from captured human-object interactions. Contact-aware retargeting and approach-motion completion produce robot references, while a shared policy uses contact and object geometry to control the body and fingers across multiple objects.

HuMiT is a whole-body teleoperation system that drives a humanoid from sparse human keypoints alone, achieving  20 ms communication-to-inference latency to enable reactive whole-body skills such as ball catching and loco-manipulation.

Developed a Whole-Body Model Predictive Control (WBMPC) framework to manage task priorities and scheduling in multi-task mobile manipulation scenarios. Integrated task priorities into a unified weight matrix, enabling smooth transitions across tasks in both spatial and temporal dimensions.