CynLr.

Enabling robots to see, understand and learn to grasp and manipulate objects, even from clutter !

Why we must solve this?

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31% Time Spent on Manual Labour

Almost one-fifth of the time spent in US workplaces involves predictable Physical Activity and is prevalent in such sectors as Manufacturing and Retail Trade. Accordingly, these sectors have a relatively high technical potential for automation using today’s technology.(McKinsey).

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Why we must solve this?

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Adaptive & Coordinated Acquisition

See to manipulate, manipulate to see

Vision for object manipulation requires a dynamic image acquisition hardware, with the ability to focus, refocus, pan, zoom, move around in real-time, and adaptively acquire at high speeds.

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Multi-Dimensional Perception

Not 2D, not 3D, we see in 10D

Static 2D color images and 3D depth maps alone are insufficient inputs for neural networks to model the complexities of objects. Our imaging pathway constructs up to 10 dimensions of information, dramatically reducing the quantum of data for learning.

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Stunning layouts for your app

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Versatile Robot Arm with
Adaptive Gripping

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Multiple-dimensional
Camera H/W

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Reinforcement Learning
for Object Modelling

Customer Testimonials

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Investors

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Latest from the Blog

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