By CIO Insider Team
 

FieldAI, an Irvine, California-based robotics startup, has raised $ 405 million across multiple previously undisclosed rounds to develop what it calls foundational embodied AI models essentially robot brains designed to help everything from humanoids to quadrupeds to self-driving cars adapt to new environments.

The company announced the funding Wednesday; the most recent round raised $314 million in August and was co-led by Bezos Expeditions, Prysm, and Temasek.

FieldAI’s other backers include Khosla Ventures, Intel Capital, and Canaan Partners, among others.

Unlike traditional AI that processes text or images, embodied AI refers to AI that controls physical robots moving through real-world environments.

FieldAI builds Field Foundation Models, which are general-purpose embodied AI models rooted in physics. This approach gives robots the ability to quickly learn and adapt to new environments while being conscious of risk, FieldAI founder and CEO Ali Agha told TechCrunch.

“The goal is to create one robotic brain that can adapt to various robot kinds and a wide range of settings,” Agha stated.

Also Read: No More FASTag? What is GPS-Based Toll Collection?

"To reach that point, you must handle risk and safety while moving into these unfamiliar settings." This has created a significant gap in robotics because conventional models and methods were not intended to address that risk and safety.

Agha stated that incorporating a layer of physics into these AI models is crucial for enabling robots to safely learn in unfamiliar surroundings. This enhancement provides robots with an additional source of information for decision-making—particularly in unfamiliar settings—rather than merely responding to the next action dictated by a model, as conventional LLMs operate.

He remarked that although a slight degree of AI hallucination may not be harmful in specific situations, it can pose risks for robots operating in hazardous settings or in proximity to humans.

Also Read: AI Appreciation Day: From Innovation to Transformation

“Unexpectedly, you begin to feel this awareness of what I know, and if there’s something I’m unaware of, or when I make a choice, how sure I feel about it,” Agha states.

"When the network gains access to that, it begins to make significantly safer choices." Not only does this produce a response indicating, ‘Here’s the next kind of action,’ but it also conveys its level of confidence, allowing you as a customer to set the risk threshold, with the robot adapting to that.