One paper accepted to Science Advances


In our latest work published in Science Advances, our group presented an automated system that designs and 3-D prints complex robotic actuators which are optimized according to an enormous number of specifications. We demonstrate the system by fabricating actuators that show different black-and-white images at different angles. One of our actuators portrays a Vincent van Gogh portrait when laid flat and the famous Edvard Munch painting “The Scream” when tiled an angle. We also 3-D printed floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids.

The research paper Topology optimization and 3D printing of multimaterial magnetic actuators and displays was published in Science Advances last Friday, and MIT News covered our story on the same day.

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Paper accepted to UIST 2019

Alex's latest work Knitting Skeletons: A Computer-Aided Design Tool for Shaping and Patterning of Knitted Garments is officially accepted to UIST 2019. In this paper, Alex and Liane present a novel interactive system for simple garment composition and surface patterning. Both casual users and advanced users can benefit from their system. Check out these beautiful samples to explore the new possibility brought by their tool!

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.