Shaoxiong (Shawn) Wang

Ph.D. Candidate @ MIT CSAIL

Email: wang_sx [at] mit (dot) edu
Google Scholar / CV

I'm a Ph.D. student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT) supervised by Prof. Edward H. Adelson. I also have the opportunity to work with Prof. Alberto Rodriguez. I recieved my B.E. degree from Department of Computer Science and Technology, Tsinghua University.


I study Tactile Perception & Manipulation, Computer Vision, and Machine Learning.


Previously, I was fortunate to work in Knowledge Engineering Group, Tsinghua University, advised by Prof. Jie Tang; Big Data and Social Computing Lab, University of Illinois at Chicago, advised by Prof. Philip S. Yu; Facebook AI Research, advised by Dr. Roberto Calandra.




Publications


SwingBot: Learning Physical Features from In-Hand Tactile Exploration for Dynamic Swing-up Manipulation

IROS'20 Best Paper Award
[Paper] [Project Page] [MIT CSAIL News]


Cable Manipulation with a Tactile-Reactive Gripper

RSS'20 Best Paper Award Finalist
[Paper] [Project Page] [MIT News]


3D Shape Perception from Monocular Vision, Touch, and Shape Priors

IROS'18
[Paper] [Project Page]


Active Clothing Material Perception using Tactile Sensing and Machine Learning

ICRA’18
[Paper][Data] [Project Page]



Connecting Look and Feel: Associating the visual and tactile properties of physical materials

CVPR’17 Oral
[Paper][Data][Project Page]


Active Zero-Shot Learning

CIKM’16
[Paper][Data]

Side Projects


MIT 6.943[J] How To Make (Almost) Anything

Teleoperation with force feedback.

Features:

         Micro-controller : ATmega16U4

         USB-support : Based on LUFA USB library

         Force Sensing : Hall-effect sensors & magnet

         3D-Printed : Cable-driven robot hand, Yale Openhand
[Video] [Website]


Music Generation with Recurrent Neural Network

1st Place in Microsoft Campus Elite Program  News
Grand Prize in 35th Tsinghua Challenge Cup (top 6 among 381 teams)   News

A comprehensive music generation system based on recurrent neural network. It generates music automatically driven by data and is embedded with rich music knowledge.



Shaoxiong (Shawn) Wang 2020