The Computational Fabrication Group at the MIT Computer Science and Artificial Intelligence Laboratory investigates problems in digital manufacturing and computer graphics. The group is led by Professor Wojciech Matusik.


Paper published in Nature

Our latest work on human grasping "Learning the signatures of the human grasp using a scalable tactile glove" was published in Nature. In this paper, we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp—through the lens of an artificial analog of the natural mechanoreceptor network—can thus aid the future design of prosthetics, robot grasping tools, and human-robot interactions.

The paper was led by Subramanian Sundaram, who obtained his Ph.D. from our group in 2018 and continued this work after his graduation.

One paper accepted to SIGGRAPH 2019

Our PhD student Jie Xu will present his latest work "Learning to Fly: Computational Controller Design for Hybrid UAVs with Reinforcement Learning" at SIGGRAPH 2019 this summer. In this paper, Jie proposed a novel neural network controller design for hybrid UAVs, an aerial robot that is challenging to control due to its complex aerodynamic effects. His method allows us to directly apply a controller trained in simulation to real hybrid UAV hardware without any modification. Checkout his website and the project page to find more!

One paper accepted to ICML 2019

Alex and Tae-Hyun's latest paper on neural inverse knitting is accepted to ICML 2019. In this paper, they introduce the new problem of automatic machine instruction generation using a single image of the desired physical product. Check out the project page to learn more about the way they tackle the problem and see a dataset of real knitting samples!

Tae-Hyun Oh joined FAIR

After spending one and a half wonderful years in our lab, our very own Postdoctoral researcher, Tae-Hyun Oh, joined Faceboook AI Research, Cambridge, MA, which is located across the street. During his stay at MIT, Tae-Hyun has contributed to a wide range of research projects, including Motion MagnificationSoft SegmentationSpeech2Face, and Inverse Knitting. Congratulations to Tae-Hyun, and wish him the best of luck in the future!

One paper accepted to ICRA 2019

Yuanming's latest paper ChainQueen was accepted to ICRA 2019. In this paper, he presented a differentiable deformable body simulator that opens up lots of possibilities for efficient controller and geometry design algorithms in robotics. Check out this video to see what amazining work can be done using his simulator!