Toru Lin

Toru  Lin
MIT EECS | Aptiv Undergraduate Research and Innovation Scholar
Advisor: Antonio Torralba
Department: EECS
Years: 2018-2019
Research Project Title:

Learning Particle Dynamics in Partially Observable Scenes

abstract:The goal of this project is to build a model that is capable of generating particle representation and predicting the dynamics of partially observable physical scenes. This is a step forward from Dynamic Particle Interaction Networks (DPI-Net), a particle-based simulator that can only learn object dynamics and make predictions based on fully observable scenes. Upon successful implementation of the new model, we will be able to create robots that can quickly adapt to new environments with unknown dynamics and accomplish various real-world control tasks using less computation.

"I am excited about SuperUROP because it will allow me to gain a year-long experience in academic research. This project interests me a lot because it lies in the intersection between robotics and artificial intelligence. Having worked as a software engineer, I look forward to utilizing my engineering skills and diving deeper into the optimization of robotic control with the aid of machine learning."