Frodobots, a robotics startup that collects data from the crowd and uses it to build BitRobot – a Solana based network for embodied AI.
BitRobot aims to link different subgroups of robotics researchers using a token-based network and shared token to address resource allocation issues and scarcity. Protocol VC was the leader of this funding round. Other participants included Big Brain Holdings and Solana Ventures. Frodobots had previously raised $2 million as pre-seed financing.
Frodobots began by building robots to walk along city sidewalks. These robots slowly traversed the streets of cities around the world, gathering crowdsourced data such as audio, GPS and video. The project’s goal is to use token incentives in order to be a part of DePIN (decentralized physical infrastructure).
Frodobots has expanded its ambitions in recent months. The co-founder of the lab, Michael Chung Yeung Cho, said that the ultimate goal was to now launch BitRobot. “solve embodied AI.” Frodobots are a part of BitRobot’s network.
“If we were to be successful, we would be on par with the Teslas and the DeepMinds of the world,” Cho also added that the new project could help create a “series of really performant foundational robotics models that can be deployed across all kinds of robots.”
Cho and Jonathan Victor said that there is not enough data to train large language models, but most robotics businesses outside of Tesla’s self-driving unit are only working with subsets. Victor explained that large language models can train using the internet, whereas most robotics companies outside of Tesla’s self-driving division are not. “starting effectively from zero.”
This is partly because a digital benchmarking of a new AI-model, for example, can be done in just an hour. Robotics, however, has to wait until products are tested in real life. Cho says that it’s impossible to know whether a self driving car is really working until the vehicle has been driven for one trillion miles.
BitRobot creates a network that is shared by all users. “subnets” Using pooled data and resources, robotics can be created more effectively. A document sent to Blockworks cites as examples subnets: humans, fleets humanoid bots and Frodobots’ sidewalk robots.
BitRobot could allow researchers to share massive resources according to their need. The DePIN token rewards system would align economic incentives. There is also cross-embodiment learning, which suggests that AI models trained on one type of robot — such as a robotic arm — can effectively transfer learned skills to entirely different robotic systems, like drones or wheeled robots. It works because robotic tasks have similar design requirements.
In a couple of weeks, a whitepaper containing additional details about BitRobot and the tokenomics for the project will be published.
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