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Teleo It describes itself as a construction robotics startup, but its mission is bigger than automating heavy equipment like excavators and tractors. Today, Teleo’s updated machines allow its customers to operate their existing fleets semi-autonomously. In the future, the startup sees the data it collects as a key enabler for the robotics industry to reach the “ChatGPT moment.”
This is not an ambition to reach the same level of hype surrounding ChatGPT. Instead, Vinay Shet, CEO of Teleo, sees an opportunity for bot companies — namely the ones he runs — to collect huge datasets similar to the amounts used to build ChatGPT in order to make big, game-changing leaps in bots.
Investors seem keen to help the startup reach this milestone. TechCrunch has learned that Teleo recently raised $16.2 million in funding through two extensions for its 2022 Series A round. The $9.2 million extension closed in April and 7 million dollars One closed this week, according to recent filings and information from the company.
“The underlying models that led to the creation of ChatGPT relied heavily on trillions of data tokens that were freely available on the internet, language, videos, images, etc. This data does not exist in bots,” Vinay Shet, CEO of Teleo, told TechCrunch. “The best dataset we know of in the robotics world is about 2.4 million symbols, while in the language world, they train it on trillions of symbols.”
Teleo aims to fill this gap by recording data from its daily operations, which Chait says will eventually “end up being the basis on which you can train real robotic foundation models” that can lead to generalized intelligence.
To build this data repertoire, Teleo needs to deploy quickly, at scale, and across many industries. The company’s strategy for doing this is its semi-autonomous approach. Teleo can update any piece of equipment with the necessary self-driving software and sensors — such as cameras, sensors, and radar — to be able to drive itself autonomously in limited conditions. Human operators then step in remotely to perform more complex tasks, such as unloading a dump truck, and can usually handle multiple vehicles at once.
“This combination is what allows us to solve the customer’s full use case while delivering the customer (return on investment) and making money with a standalone product,” Chait said.
In order to maintain a diverse data set, Teleo recently Expanded It goes beyond building and construction, deploying autonomous heavy machinery, such as wheeled loaders, dozers and excavators, across a range of industries, including pulp and paper, logging, port logistics, agriculture and munitions removal. Teleo also targets industries such as airports, waste and recycling, logistics and snow removal.
The hope is that the data it collects — including input from human operators, video footage, and sensor reactions — will allow Teleo to fine-tune or customize its basic robot models. This may eventually enable the human-in-the-loop to be replaced or augmented with a cloud-based AI agent capable of learning to control different machines, as a human would.
There is no doubt that this long-term thinking is what attracted investors to the company. Teleo’s recent expansions were led by UP.Partners with participation from new investors Trousdale Ventures and Triatomic Capital, as well as returning investors F-Prime Capital and Trucks VC, among others.
Teleo says the funds will be used to scale customer deployments, further expand into new industries, and enhance the startup’s AI capabilities, including the integration of large language models (LLMs) to unlock operator efficiencies.
“Over the next several years, you’ll see vertically integrated companies like ours actually spread out into the real world in a way that makes economic sense and grow economically based on that,” Chait said. “But along the way, they will collect enough data in the right format that they will be able to unleash an ‘aha’ moment a few years later.”
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