RIOS raises funding, emerges from stealth with dexterous robots


RIOS lab automation

DX-1 is able to recognize and handle parts in multiple orientations. Source: RIOS

RIOS Corp. today emerged from 18 months of “stealth mode” and announced that it has raised $5 million in venture funding. The startup said it has developed “highly dexterous robots that handle hard-to-automate tasks in unstructured environments.”

Palo Alto, Calif.-based RIOS spun out of Stanford University and was founded in 2018 by former Xerox PARC engineers. The company claimed that not only can it automate individual lines with its artificial intelligence and manipulation systems, but it can also build “lights-out” factories with its network of systems integrators.

“We’re building robots of the future — ones that can learn on the job, construct models of the world, and extend these models to perform different tasks,” stated Dr. Bernard Casse, founder and CEO of RIOS.

RIOS tackles complex tasks

“Our sweet spot is complex automation — i.e., automation in unstructured environments that is hard to automate and that typically requires human-level dexterity,” Casse told The Robot Report. “By ‘hard to automate in unstructured environments,’ we mean handling hundreds of SKUs, precise manipulation and assembly, sorting and selecting objects coming at different positions on an assembly line, etc.”

“If a task on an assembly line cannot be currently automated by any of the ‘big four’ robotics companies [ABB, FANUC, KUKA, and Yaskawa] and would require millions of dollars in customized engineering solutions that would require an overhaul of the factory, this is when RIOS comes in,” he said. “Our robot can be dropped into an existing assembly line to perform many types of tasks.”

“With a first-of-its-kind haptic intelligence platform combining dedicated hardware, computer vision, and AI, our robots can learn to grasp and handle many types of objects, precisely selecting and assembling parts and performing complex manipulation tasks,” he said.

Casse cited the example of assembling an oil filter (see video below). “We provide the robot with three to four components, and it takes them and reassembles them,” he said. “It doesn’t matter the orientation of the parts or their physical location; it will still manage to assemble them.”