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We have updated the research project offers for current NYU students. Please e-mail the listed contacts directly.
Please tell us your motivation to the specific project, your skillsets and past experiences + something that shows that (CV, website, photos, videos, github, etc), and your transcript.
Students from all research backgrounds are welcome – for example, mechanical engineering, bioengineering, computer science, materials etc.
Updated July 2026
Closed-Loop Control System for Undulatory Amphibious Robots with Fluid Center of Mass
Description — Continuing our previous work on the use of mass transfer in peristaltic robots for locomotion optimization in unmodelled terrains, this project seeks to integrate a robust closed-loop control system into the project and study the usefulness of mass shifting in autonomous navigation in unmodelled terrains. By embedding a variety of sensors (IMU etc.) in existing robotic platforms and establishing a control system that recognises terrains and adapts to them, the research will create a framework for autonomous navigation of amphibious robots with a fluid center of mass.
Requirements — Control systems experience, sensor fusion, embedded systems knowledge, strong programming skills (Python, C++). Machine learning (ML) experience is a bonus, but not required.
Workload — 20% embedded system development, 60% programming and control, 20% testing and optimization
Contact — Daniil Filimonov / df2789@nyu.edu
Wi-Fi Backscattering for Wireless and Batteryless Shape Estimation of a Soft Silicone Tissue
Description — Wi-Fi backscattering is a method of transmitting data wirelessly and free of battery. Instead of signal generation, these devices harvest Wi-Fi waves and communicate with reflection and slight alternation of those waves. In this project we aim to develop a signal reflective device embedded into a silicone mold and an ML model capable of recognizing shape based on reflected signal and study its potential applications in soft robotics.
Requirements — Circuit design, signal processing, and electromagnetic (EM) physics, strong programming skills (Python, C++). Machine learning (ML) experience is a bonus, but not required.
Workload — 60% simulation development, 20% ML model development, 20% testing and optimization
Contact — Daniil Filimonov / df2789@nyu.edu
Project archive
Project archive
Coupled Jetting and Tentacle Swimming in a Squid-Inspired Robot [filled]
Description — Cephalopods are known for their unique use of tentacles and jetting to achieve a wide variety of maneuvers. Many robotic platforms have explored jetting and tentacle swimming for locomotion individually, but the interplay between the two modes remains underexplored. In this project, we propose integrating the two locomotion methods to create a robot that combines jetting with tentacle swimming to explore the fluid dynamics of a soft tentacle in a controllably turbulent environment. This project will involve prototyping a robot capable of jetting and tentacle swimming, using PIV to investigate the coupled fluid dynamics, and using simulation/optimization techniques to design a controller for the robot.
Workload — 45% design and fabrication, 45% experimental testing, 10% control/optimization
Requirements — strong hardware and electronics skills, programming experience (MATLAB, python, experience working with microcontrollers preferred), interest in fluid dynamics and soft robots.
Contact — Magan Lee / ml10362@nyu.edu
Sensor-Integrated Soft Handle for Ergonomic Navigation [filled]
Description — White canes are widely used as mobility aids for people with visual impairments. However, they can uncomfortable for long-term use and provide limited information to the user. In this project, we propose to create a soft, sensorized cane handle, that prioritizes ergonomics and provides the user with haptic feedback about their surroundings (for example, detecting wetness of the ground or obstacles outside the canes reach). Signals will be analyzed in real time to provide useful haptic feedback (through vibration or squeezes) to the user. User testing will be conducted to assess the comfort and usability of the system compared to a standard cane.
Workload — 40% sensor integration, 40% soft wearable device design and fabrication, 20% haptic control
Requirements — strong hardware and electronics skills, CAD, programming basics (MATLAB, python), interest in wearable robots and assistive devices. Biomechanics knowledge a plus but not required.
Contact — Suzanne Oliver / sho8511@nyu.edu
Cane Without a Cane: Obstacle Detection with Soft, Sensorized Cane Handle [filled]
Description — White canes are widely used as mobility aids for people with visual impairments. However, they can be uncomfortable for long-term use and carry a risk of repetitive use injuries. In this project, we propose to create a sensorized cane handle that detects obstacles with vision and depth sensors, but is lighter and more portable than a traditional cane. Haptic feedback will be provided to the user via soft pneumatic chambers that inflate when an object is detected to mimic the sensation of hitting an obstacle with a cane. This project will involve prototyping the cane handle, actuating the soft pneumatic chambers, integrating sensors, processing camera and time-of-flight data in real time, and doing user testing to assess the comfort and usability of the end product.
Workload — 40% sensor integration and signal processing, 50% design and fabrication, 10% user testing
Requirements — strong hardware and electronics skills, CAD knowledge, programming experience (MATLAB, python, experience working with microcontrollers preferred), interest in wearable robots and assistive devices. Biomechanics knowledge a plus but not required.
Contact — Suzanne Oliver / sho8511@nyu.edu
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