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About

Shaoxiong Wang is a robotics researcher whose work sits at the intersection of tactile sensing, robot manipulation, and multimodal perception. His research focuses on enabling robots to physically understand and interact with the world through rich sensory feedback, with particular emphasis on vision-based tactile sensors and dexterous manipulation of complex objects. Wang's most influential contribution is his work on cable manipulation using tactile-reactive grippers (225 citations), demonstrating that robots can handle high-dimensional, dynamic objects through real-time tactile feedback rather than rigid mechanical aids. He has also made significant strides in tactile sensor hardware, co-developing the GelSight Wedge (128 citations) and DTact sensors, which capture high-resolution 3D contact geometry in compact form factors suitable for robot fingers. His SwingBot system (109 citations) showcased how in-hand tactile exploration can reveal physical properties like mass and center of mass, enabling dynamic manipulation strategies. Beyond touch, Wang has pioneered multimodal perception research, fusing visual, auditory, and tactile signals for more robust robotic manipulation, and demonstrated that 3D shape understanding can be achieved by combining vision, touch, and shape priors (118 citations). With over 600 cumulative citations, Wang's contributions are shaping the next generation of sensorially intelligent robotic systems.