Max Robotics — Global Service Robot Directory + US Certification

About

Depeng Kong is an emerging researcher at the forefront of intelligent tactile sensing and human-robot interaction, whose work bridges flexible electronics, bioinspired design, and machine learning to advance next-generation robotic perception. His most influential contribution, "Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding" (2023, 147 citations), pioneered an inverse design strategy that integrates deep learning directly into the sensor development pipeline, moving beyond conventional trial-and-error approaches. Building on this, Kong has championed co-design methodologies that simultaneously optimize tactile sensor hardware and neural network algorithms, enabling robots to accurately decode force location, magnitude, and dynamic touch events. His innovative work on super-resolution tactile sensor arrays demonstrates how deep learning can transcend physical hardware limitations, achieving high-resolution perception with dramatically fewer sensing nodes — a breakthrough with significant implications for humanoid robotics. Beyond sensing, Kong has contributed to medical robotics, developing the GuLiM teleoperation framework during the COVID-19 pandemic to enable non-specialist control of assistive robots. With over 300 cumulative citations and publications spanning top-tier journals, Kong's research is rapidly shaping how robots perceive and respond to the physical world.