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Haptica Robotics: Teleoperation platform for robot training

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Twin teleoperation suits gather high-quality human data for future automated manufacturing.

About the Technology

A key roadblock to improved automated robotic manufacturing systems is the lack of high-quality data for complex, touch-sensitive tasks. Most training data today is visual, and where haptic training systems do exist, they are often difficult for operators to use, impairing data quality.

Our innovation is a fabric-based haptic platform using twin teleoperation suits to enable high-fidelity data gathering. Our human-facing system leverages real-time haptic feedback and precise pressure sensing, while the robotic system operates within human perception thresholds to support dynamic physical interaction tasks. Together, this plug and play solution allows us to continuously collect human-quality physical data previously out of reach.

Our system addresses the needs of robotics companies seeking better training datasets and manufacturers who require personalized automation for dexterous tasks. First-use applications include snap-and-lock manufacturing — for example wiring and panel fitment in cars and device casings for consumer tech.

Working with the HIT Fund, the team will engage with manufacturers, robotic OEMs, and end users to better understand use cases and explore paths toward commercialization.

Team Members

Allison Okamura

Allison Okamura

PI, Professor

School of Engineering, Mechanical Engineering

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Cosima du Pasquier, PhD

Cosima du Pasquier

Postdoctoral Scholar

Mechanical Engineering

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Paola Peraza Calderon

Paola Peraza Calderon

MBA Fellow

Stanford Graduate School of Business

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