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About

Qifeng Chen is a robotics and computer vision researcher whose work spans dexterous manipulation, depth estimation, and embodied AI. His most celebrated contribution, "Rotating without Seeing: Towards In-hand Dexterity through Touch" (2023, 70 citations), demonstrates that multi-finger robot hands can achieve vision-free dexterous manipulation driven entirely by tactile feedback — a landmark step toward human-like robot dexterity. This work reflects his broader commitment to multimodal sensing, bridging touch, vision, and language to enable more capable robotic systems. Chen has made meaningful advances in 3D scene understanding, including monocular video depth estimation and active multiscopic vision systems that bring affordable 3D perception to single-camera robots. His research also tackles real-world robustness challenges, with notable work on waterdrop removal and video deblurring tailored for autonomous vehicles and robotic platforms. More recently, Chen has pushed into long-horizon robot reasoning through the ERRA architecture and open-vocabulary object pose estimation, enabling robots to interpret natural language instructions and generalize to novel objects. His exploration of deformable object manipulation with Flipbot further illustrates his range. Collectively, his portfolio — spanning perception, manipulation, and embodied reasoning — positions him as a versatile contributor shaping the next generation of intelligent robotic systems.