I’m Guangzhi Tang, a PhD candidate in computer science at Rutgers University and a member of the Computational Brain Lab (ComBra). My main research interests are neuromorphic computing and robotics. I have developed Spiking Neural Networks (SNN) solving a wide spectrum of robotic problems on neuromorphic processor with robustness, efficiency, and adaptivity.
We think the most crucial task for neuromorphic computing is to build the bridge between robotics and brain. Robotics can provide real-world interactions and intelligent applications as testbeds for brain modeling, and the brain can inspire robust and efficient solutions for robotic problems. By using SNN, we believe questions of both areas can be answered simultaneously.
Spiking-DDPG trains an SNN for energy-efficient mapless navigation on Intel’s Loihi neuromorphic processor.
Brain-inspired SNN solves the 1D SLAM problem of mobile robot using Intel’s Loihi neuromorphic processor.