Chenkai Mao

Chenkai  Mao
MIT EECS | Lincoln Laboratory Undergraduate Research and Innovation Scholar
Advisor: Daniela L. Rus
Department: EECS
Years: 2018-2019
Research Project Title:

Research on General Framework for Optimizing Robot Design with Application in Soft Robots

abstract:When designing a robot, especially soft robots, there’re usually large number of parameters (including the controller parameters and the geometric parameters) that we need to optimize for certain performances of the robot, for example the moving speed, grip efficiency and flexibility, stability and robustness etc. This project aims at developing a general simulation and manufacture framework for optimizing robot design, with applications in designing soft robots. We will implement gradient descend method, with an approach called automatic differentiation, which achieves higher computational speed and precision compared to classical methods like symbolic differentiation and numerical differentiation.
About:

I am doing EECS and physics double major, and I've been longing to apply my math and coding knowledge for real-world applications. I hope through this SuperUROP project, I can learn the basic robot design process as well as ways to uptimize and integrate the whole procedure. The possibility to actually build something with optimized design really excites me.