Vedant K. Naik

vedant(at)roboticvedant(dot)com

Michigan State University

College of Engineering

East Lansing, MI

I’m an engineer drawn to hard technical problems in robotics and automotive systems — where firmware meets dynamics. My work spans motor control, power electronics, embedded perception, and autonomous platforms.

Most recently I was on Tesla’s Drive Inverter Firmware team (Aug–Dec 2025), and before that an ADAS Sensor Hardware Engineer Intern at Lucid Motors (May–Aug 2025). At Michigan State, I research soft-robotic sensing at the Smart Microsystems Lab under Dr. Xiaobo Tan, and lead Electric Propulsion on the MSU Solar Racing Team.

What I’m working on

Traction Inverter v2 — SiC-based field-oriented-control inverter for the MSU Solar Car. First spin demonstrated at the Spring 2026 Design Day. [project]
Autonomous Blimp Robot — Hardware, micro-ROS firmware, 6-DOF dynamics, EHGO-based disturbance rejection, and a next-gen RPi CM4 + Teensy avionics platform at the Smart Microsystems Lab. [project]
Soft-Robot Proprioception — Time-Delay Neural Network compensation of nonlinear stretchable strain sensors, published in Smart Materials and Structures (2025). [paper]

Recognition

Board of Trustees Award, 2026 — recognized for academic excellence (4.0 GPA). Coverage by the MSU Board of Trustees, MSU College of Engineering, and MSU Honors College.
First Place, 2025 MSU Undergraduate Research and Arts Forum (Engineering & CS)
Gold Award, 2025 MSU College of Engineering Dean’s Showcase of Stars
Tau Beta Pi Engineering Honor Society · Multiple-time Dean’s List
Also profiled at Smart Microsystems Lab · ePowerHubs

Reach out

If you’re working on motor control, autonomous systems, embedded robotics, or anything where firmware and dynamics collide — and want to compare notes, collaborate, or talk about opportunities — I’d love to hear from you. Email is the fastest path; links below.

publications

2026

Journal

BiPneu: Design and Control of a Bipolar-Pressure Pneumatic System for Soft Robots

Yu Mei, Xinyu Zhou, Vedant Naik, and 2 more authors

IEEE/ASME Transactions on Mechatronics, 2026

To appear

Abs Video

Positive-negative pressure regulation is critical to soft robotic actuators, enabling large motion ranges and versatile actuation modes. However, achieving high-performance regulation across both pressure polarities remains challenging due to asymmetric inflation–deflation dynamics, valve nonlinearities, and switching-induced flow disturbances. This paper presents BiPneu, a compact and cost-efficient multi-channel bipolar-pressure pneumatic system for soft robots that enables wide-range, accurate, and responsive pressure regulation while providing seamless compatibility with high-level software ecosystems. A dual-mode sliding-mode controller (DM-SMC) with hysteresis-supervised mode selection is proposed based on a hybrid electro-pneumatic model. Extensive simulation and experiments demonstrate the superior performance of DM-SMC in tracking step and sinusoidal pressure references compared with both advanced model predictive controllers and well-tuned PID controllers. Experimental results show average absolute errors of 1.44 kPa in multi-step tests and 4.23 kPa in sinusoidal tracking, corresponding to reductions of 11.9% and 35.6% relative to PID control, along with improved control effort, valve switching rate, and transient response. Robustness of DM-SMC is further verified on a bellow actuator with pressure-dependent volume. Finally, BiPneu’s capability is demonstrated via two soft robotic examples, quick ball-maneuvering with a soft parallel manipulator and real-time finite element method (FEM)-based teleoperation of a soft bellows actuator.

2025

Journal

Nonlinear compensation of stretchable strain sensors with application to proprioceptive sensing of soft robotic arm

Vedant Naik, Preston Fairchild, and Xiaobo Tan

Smart Mater. Struct., Feb 2025

Abs DOI Video

Abstract With advances in materials and manufacturing techniques, recent years have seen a number of conductive composite materials that exhibit pronounced strain-dependent electrical resistivity, allowing them to be used for embedded, cost-effective strain sensing in various applications. The strain-resistivity relationship of these materials, however, is often highly nonlinear and dynamic, posing challenges for effective use of such strain sensors. In this paper, a computationally efficient scheme is proposed for compensating the nonlinear, dynamic strain-resistance behavior of a soft conductive rubber using a Time Delay Neural Network (TDNN). The accuracy and feasibility of the technique is evaluated with a soft robotic arm incorporating three strain sensors for proprioception. Experimental results show that the sensing scheme is able to predict both the tip position and the shape of the robotic manipulator, achieving an average tip positional error of less than 4% relative to the total length of the manipulator.