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Machines are now used to manufacture a wide range of items. Different machines are designed to produce and handle different parts and items. A single object, such as a washing machine or an automobile, necessitates a large number of machines. Human vision and hands are the only solutions for available adjusting to unforeseen conditions and changes in manufacturing objects.
With its visual object intelligence platform, Bengaluru-based CynLr attempts to overcome this technological gap by allowing industrial robotic arms to see, understand, and operate any object in unstructured surroundings. With little to no pre-training, CynLr’s visual robots can pick up any object of any size, shape, weight, or material, regardless of orientation or location. This is the first step toward developing Universal Factories, which are factories that are so standardized that any product – from automobiles to phones to, most likely, your food – can be created under one roof.
An object’s shape is defined by how it appears to the observer and how it seems to the observer changes with every minor change in orientation. Robots can learn to manage items in any orientation, thanks to this CynLr’s technique. It enables robots to grasp objects in unstructured environments and learn to make orientated placements to accomplish desired ends.
The CynLr visual intelligence platform combines a revolutionary combination of Auto-Focus Liquid Lens Optics, Optical Convergence, Temporal Imaging, Hierarchical Depth Mapping, and Force-Correlated Visual Mapping to enable robots to operate objects even in extremely unpredictable circumstances.
CynLr has raised $4.5 million in Pre Series A funding round led by Speciale Invest and growX Ventures. Anicut Capital, Java Capital, Infoedge, Venture Catalysts, and Arali Ventures were among the other institutional investors in the round. The funding will be used to establish CynLr’s footprint in the United States, grow its team to over 50 individuals, and increase its capacity to serve clients and deliver 100 robots every year.