Category: Synthetic Data for Robots

Synthetic Data for Early Robots, Part II: MOBOT and the Problems of Simulation

Last time, we talked about robotic simulations in general: what they are and why they are inevitable for robotics based on machine learning. We even touched upon some of the more philosophical implications of simulations in robotics, discussing early concerns on whether simulations are indeed useful or may become a dead end for the field. Today, we will see the next steps of robotic simulations, showing how they progressed after the last post with the example of MOBOT, a project developed in the first half of the 1990s in the University of Kaiserslautern. This is another relatively long read and the last post in the “History of Synthetic Data” series.

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Synthetic Data for Robots, Part I: Are Simulations Good For Robotics?

In the previous two blog posts, we have discussed the origins and first applications of synthetic data. The first part showed how early computer vision used simple line drawings for scene understanding algorithms and how synthetic datasets were necessary as test sets to compare different computer vision algorithms. In the second part, we saw how self-driving cars were made in the 1980s and how the very first application of machine learning in computer vision for autonomous vehicles, the ALVINN system, was trained on synthetic data. Today, we begin the discussion of early robotics and the corresponding synthetic simulators… but this first part will be a bit more philosophical than usual.

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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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Synthesis AI speaking at the MetaBeat conference on Oct 4th

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