Category: Research Topics

June 29, 2020
Synthetic Data Research Review: Context-Agnostic Cut-and-Paste

We have been talking about the history of synthetic data for quite some time, but it’s time to get back to 2020! I’m preparing a new series, but in the meantime, today we discuss a paper called “Learning From Context-Agnostic Synthetic Data” by MIT researchers Charles Jin and Martin Rinard, recently released on arXiv (it’s less than a month old). They present a new way to train on synthetic data based on few-shot learning, claiming to need very few synthetic examples; in essence, their paper extends the cut-n-paste approach to generating synthetic datasets. Let’s find out more and, pardon the pun, give their results some context.

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March 6, 2020
ClearGrasp: Our Collaboration with Google Robotics

Optical 3D range sensors, like RGB-D cameras and LIDAR, have found widespread use in robotics to generate rich and accurate 3D maps of the environment, from self-driving cars to autonomous manipulators. However, despite the ubiquity of   these complex robotic systems, transparent objects (like a glass container) can confound even a suite of expensive sensors that are commonly used.

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