"Your first Personal Robot"
YC F24 startup building teachable general-purpose home robots programmable via code, natural language, and physical demonstrations.
Innate (founded 2024, Palo Alto, CA; YC F24) develops general-purpose personal robots designed to be as easy to program as AI agents. Its first platform, Maurice, is a mobile robot with a trainable arm that combines an onboard AI brain fusing nine models (including GPT, Gemini, Claude, and Llama) for navigation, manipulation, reasoning, and human interaction. Users teach new skills in under 30 minutes via code, system prompts, or physical demonstrations.
The open SDK lets makers build and share custom behaviors. Founders: Axel Peytavin (CEO) and Vignesh Anand (CTO), both former Stanford AI/robotics researchers. Raised $500K seed from Y Combinator.
Primary type & automation activities this supplier delivers:
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WEBSITE
https://innate.botHEADQUARTERS
United States
Company Facts
Founded
2024
Primary Role
Manufacturer
Company Size
-
Primary Region
North America
Annual Sales
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Funding Stage
-
Funding Total
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In early 2026, Acorn Robot diverged from mainstream narratives in embodied intelligence, which focused on VLA (Vision-Language-Action) models. The company, founded by a Tsinghua University mechanical engineering professor and his eight doctoral students, is exploring a unique approach centered on operational instincts. This 'bottom-up' strategy aims to endow robots with inherent operational capabilities through interaction with the physical world, rather than relying on extensive data and large models. This innovative direction is significant as it challenges the conventional 'imitation learning' paradigm. Acorn Robot posits that operational behaviors may stem from innate 'internal expectations' rather than explicit action planning. By embedding these instincts into robots, the company believes they can develop stable operational skills autonomously, without needing to mimic vast amounts of human data. As the industry progresses, attention will be on how Acorn Robot's approach addresses challenges such as data scarcity, real-time execution constraints, and the need for hardware-specific adaptations. No further timeline was disclosed at the time of publication.
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