CynLr

Solving for the
Enabling robots to see, understand and learn to grasp and manipulate any object even from clutter!
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Automate any

Task without any customized setups for structuring

Pick

any object
Incoming-box
Orient

from all positions
Place
in every way

Why we must solve this?

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Robot Automation Gives Rocket-boost to Economies & Per-Capita GDP

World GDP grew only proportionally with population until before we began automating. Coinciding exactly with the 2nd industrial revolution circa 1870s, and subsequently with the adoption of robots and large-scale automation post 1950s, GDP has grown 6X more than population.

We must adopt robots rapidly to achieve phenomenal economic growths

World Spends Trillions on Manual Labour while Robots Market Still Grows at Snails Pace

Contrary to popular perception, while US alone spends a whopping $1.3T on manual labour wages, the world spends only $16B in deploying industrial robots and $32B in robot accessories and services every year. Industrial Robots market is growing at a modest 8% CAGR while Internet Apps are disrupting at 30% CAGR!

A significant technological breakthrough is required to enable faster adoption of robots.

Image

Yet, Still 31% Time Spent on Manual Labour

According to a McKinsey study on the state of automation, even today, 31% of the time spent by the world labour force is on performing manual labour task. This is the single largest category of jobs performed.

The technical feasibility of automating these tasks is still below 50% wherever there is any unpredictability in the tasks.

The technical feasibility of automating these tasks is still below 50% wherever there is any unpredictability in the tasks.

Enabling robots to comprehend and adapt to unpredictability remains elusive.

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I think robots that have vision and manipulation as good as humans is a huge milestone that will happen in the next decade and is being underestimated.

– Bill Gates, Reddit AMA, 2016

I think grasping is going to be a solved problem in the next 10 years. It’s turned out to be an incredibly difficult problem, probably in part because we’re starting to solve it with machine vision, so (that means) machine vision did have to come first.

– Jeff Bezos, Amazon re:MARS, 2019

” Human-like Robot Versality Requires a Human-like Robotic Vision System “

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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” Moonshots require strong foundations, solid partnerships, and a high-performing team. Also, some persistence & hard work.”

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Our insights into the manual labor and automation market and the cutting edge advances in robotics.

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