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Jacobi Robotics Launches with $5 Mn Seed & Motion Planning Breakthroughs

Jul 17, 2024
  • A 95% reduction compared to traditional robot programming methods, Jacobi has set a new industry standard for robot deployment time.
  • Founded by a group of world-leading roboticists and AI researchers from UC Berkeley and Carnegie Mellon University, Jacobi’s founding team includes Yahav Avigal, Lars Berscheid, Max Cao, Jeff Ichnowski and Ken Goldberg.

Programming an industrial robot can take weeks to months. Jacobi Robotics has pioneered a new kind of AI-powered motion planning technology that requires 1000x less computational time and enables the deployment of robots in less than one day compared to one month with existing programming tools. Today, the startup launched the availability of its groundbreaking technology and robotics AI software platform, and announced it has raised a $5 million seed round led by Moxxie Ventures with participation from Foothill Ventures, Humba Ventures, and The House Fund. Existing investors Swift Ventures, Berkeley SkyDeck Fund, and LDV Partners also participated.

The startup also announced the availability of the Jacobi Palletizer, the first in a series of AI-powered solutions that leverage Jacobi’s breakthrough motion planning technology. More than $400 billion worth of US trade is exported annually on pallets, driving a rapidly growing demand and deployment for robotic palletizers that stack cases onto pallets for storage and shipment.

With recent advances in AI combined with increasing labor shortages, industrial robotics has reached an inflection point. Robots are now being reprogrammed every six months instead of every six years. Shorter production runs and new use cases enabled by advancements in AI require robots that can adapt to changes. In production deployments, Jacobi’s partners have already seen a 95% reduction in deployment time and a 24% savings in overall project costs after deploying Jacobi.

“Just a few years ago, machine learning was limited to a small group of experts and researchers,” said Max Cao, CEO and co-founder of Jacobi Robotics. “Today, any software developer can train a production-ready machine learning model by following an online tutorial. Similarly, in a few years, software engineers will be implementing production-ready robot applications using Jacobi.”

“The strong demand we’ve seen from the market underscores how programming for industrial robots is ripe for a massive shift,” said Cao. “The new financing will enable us to quickly ramp Jacobi’s capacity to meet initial and new demand fulfillment.”

Robot motion planning, which involves controlling a robot’s joints to maneuver its end-of-arm tool while avoiding obstacles, is a foundational aspect of robotics. However, until recently, it was virtually never used in commercial settings due to the complexity and computational requirements of implementing these algorithms, making them impractical. Jacobi has pioneered a new kind of AI-powered motion planning technology resulting in the fastest ever real time collision-free trajectory generations with computational times below 1 ms.

“The founders of Jacobi Robotics are leading roboticists hailing from the Berkeley AI Research Lab, acclaimed for their trailblazing research in AI, optimization, and motion planning,” said Alex Roetter, General Partner of Moxxie Ventures. “They have effectively leveraged their academic roots to rapidly build a company that addresses critical needs in the robotics space. Their comprehensive understanding of both the technical and commercial aspects of robotics has helped them design a platform that simplifies programming for industrial robots.”

Jacobi’s robot motion planning technology has sparked significant demand, with agreements already secured with manufacturers across the US, top robotics solutions providers including Formic, and Fortune 500 companies worldwide across a wide range of sectors including electronics and consumer packaged goods (CPG).

The post Jacobi Robotics Launches with $5 Mn Seed & Motion Planning Breakthroughs first appeared on AI-Tech Park.


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[Source: AI Techpark]

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