Max Robotics — Global Service Robot Directory + US Certification

About

Manas Gupta’s research bridges the frontiers of neural network theory, robotics control, and medical technology. His foundational work on feedback-error learning with recurrent neural networks (1994, 13 citations) pioneered methods for driving unknown nonlinear dynamic systems toward desired trajectories, establishing a paradigm for adaptive control. Gupta further advanced the field by developing the dynamic neural unit (DNU, 2002, 6 citations), a biologically inspired neuron model that optimizes feedforward and feedback synaptic weights for robotics and control applications. His contributions extend to inverse kinematics for multi-linked robots (1993, 3 citations), demonstrating practical implementations of dynamic neural networks in robotic manipulation. Notably, Gupta’s work on CyberKnife technology (2015, 4 citations) highlights his impact on precision surgery, detailing how stereotactic radiosurgery delivers targeted radiation to tumors while sparing healthy tissue. His most recent research introduces Traversability-based Control Barrier Functions (T-CBF, 2025, 2 citations), redefining safety in mobile robot navigation beyond collision avoidance to address vertically challenging terrain. With a career spanning three decades, Gupta’s interdisciplinary approach—from neural control theory to clinical applications—has influenced both academic research and real-world medical robotics, earning him recognition as a versatile innovator in intelligent systems.