Cewu Lu

FA-RDP achieves superior success rates in contact-rich manipulation while preserving diverse action modes, revolutionizing how we approach multimodal decision-making in robotics.

Calibration-free dexterous hand retargeting achieves intuitive control and superior performance without the need for hand-specific tuning.

ChronoFlow-Policy outperforms traditional methods by effectively unifying past and future interaction dynamics, enhancing performance in complex manipulation tasks.

Agents using a structured memory framework can achieve significantly better manipulation performance, outperforming traditional methods in task completion and skill generalization.

Learning symbolic POMDPs from visual demonstrations allows robots to plan effectively under uncertainty, outperforming traditional methods in complex tasks.

FTP-1 not only excels on familiar tactile sensors but also achieves unprecedented success on unseen setups, redefining the potential for cross-sensor generalization in robotic manipulation.

SyVLA achieves unprecedented task success rates and generalization in real-world robotic applications by effectively decoupling intention from control.

A novel representation for articulated parts perception achieves 73% manipulation success without the need for extensive fine-tuning.

Forget generic retrieval signals – UniDoc-RL uses reinforcement learning to teach LVLMs how to actively perceive and reason about visual information, yielding a 17.7% performance boost.

Robots can now learn complex manipulation tasks directly from human demonstrations using only a pair of smart glasses, achieving zero-shot transfer without specialized hardware.

Robots can now perform contact-rich tasks with significantly improved success rates and reliability by explicitly reasoning about forces, outperforming prior methods by up to 48%.

Unlock human-like dexterity in robotic manipulation by combining RL-assisted teleoperation with a novel VLA architecture that leverages force and tactile feedback.

Imagine fixing your robot's mistakes *before* it even makes them: RoboPocket lets you train robots twice as efficiently using just your smartphone and AR.