Origami Robotics, a Silicon Valley–based robotics startup building high-dexterity robotic hand systems and data infrastructure for general-purpose manipulation, has raised seed funding as it develops its approach to embodied artificial intelligence and real-world robotic learning systems.

The company focuses on solving one of the core challenges in robotics: enabling machines to perform complex, human-like manipulation tasks in unstructured environments. Its system combines high-degree-of-freedom robotic hands with purpose-built data collection devices designed to capture precise human hand movements. This co-designed hardware approach is intended to close the “embodiment gap” between human motion data and robotic execution, improving how robots learn fine motor skills from demonstration.

Origami Robotics was founded in 2026 by robotics researchers Daniel Xie and Ryan Xie. The company is headquartered in Millbrae, California, and is building a small but specialized engineering team focused on robotic hardware design, motion capture systems, and machine learning infrastructure for physical AI applications.

The company has raised approximately $500,000 in seed funding in its first known external financing round. The capital is being used to accelerate development of its robotic hand platform, expand its data collection systems, and support early collaboration efforts with robotics research labs and physical AI developers working on manipulation learning systems.

The seed round was led by Sequoia Capital, a global venture capital firm known for investing in early-stage and growth-stage technology companies across AI, robotics, and infrastructure. Participation from Sequoia reflects growing investor interest in embodied AI systems and dexterous robotics technologies aimed at general-purpose automation.

Additional participation came from Andreessen Horowitz (a16z), a venture capital firm that has actively invested in robotics, artificial intelligence, and frontier technology startups focused on machine learning-driven automation and physical intelligence systems.

Origami Robotics’ technology stack integrates robotics hardware, sensor systems, and machine learning pipelines designed to support large-scale training of manipulation models. Its robotic hands are engineered with high degrees of articulation to replicate the complexity of human hand motion, while its data collection systems are designed to ensure that training inputs are structurally aligned with robotic output capabilities.

The company’s approach is centered on improving data quality for robotic learning, a key bottleneck in scaling general-purpose manipulation systems. By capturing high-fidelity motion data through purpose-built devices rather than repurposing consumer-grade motion capture systems, Origami Robotics aims to improve model performance and reduce the gap between simulation and real-world deployment.

With its seed funding in place, the company plans to expand its engineering efforts, refine its hardware design, and scale its data infrastructure for training next-generation manipulation models. It is also exploring partnerships with academic and industrial research groups focused on physical AI, robotics dexterity, and reinforcement learning for real-world environments.

The broader robotics industry is undergoing rapid expansion as advances in AI make it possible for machines to learn increasingly complex physical tasks. Within this landscape, Origami Robotics is positioning itself at the intersection of hardware innovation and machine learning infrastructure, targeting the foundational challenge of enabling robots to manipulate diverse objects with human-like dexterity.

As investment in embodied AI continues to accelerate, Origami Robotics is aiming to establish itself as a core infrastructure provider for general-purpose robotic manipulation systems, with its seed funding supporting early development and experimental deployments across research environments.

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