Baojiang Li
Papers
6
Total Citations
50
H-Index
4
About
Baojiang Li is an emerging researcher at the intersection of robotics, multimodal perception, and intelligent prosthetics, whose work is rapidly gaining recognition in the field of human-robot interaction and sensory fusion. Li's most celebrated contribution is the TVT-Transformer, a tactile-visual-textual fusion network for object recognition that has already garnered 20 citations since its 2025 publication, signaling strong community interest in multimodal approaches to robotic perception. Building on this, earlier works such as the DT-Transformer and shape-texture fusion networks demonstrate Li's sustained effort to overcome the limitations of single-modality tactile recognition, cleverly leveraging text as a scalable alternative to image-constrained datasets. Beyond perception, Li has made meaningful strides in prosthetics and motor control. Research into reinforcement learning-based bionic hand motion and shoulder-amputated prosthesis control reflects a commitment to restoring human function through intelligent algorithms, with these papers accumulating 15 citations combined. Work on dexterous grasping via deep reinforcement learning further underscores a coherent research vision spanning sensing, understanding, and action. With a growing body of work across tactile intelligence and adaptive robotics, Li represents a compelling voice in next-generation human-centered robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3DT-Transformer: A Text-Tactile Fusion Network for Object Recognition7 citations · 2024
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