This company provides robotic training data generated at scale by connecting robotics firms to a network of human operators. These operators collect real-world, in-the-wild data essential for improving robotic perception and control systems. Sensei facilitates the acquisition of diverse, labeled datasets necessary for robust machine learning in robotics applications.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Robotics companies face a significant challenge in acquiring sufficient, high-quality training data for robotic manipulation tasks. Traditional data collection methods are often expensive, time-consuming, and lack the diversity needed to train robust AI models. This data scarcity hinders the development and deployment of advanced robotic systems.
Solution
Sensei offers a teleoperation platform that connects robotics companies with a network of human operators to efficiently collect diverse, labeled training data. By leveraging human expertise, Sensei enables the generation of in-the-wild demonstration data tailored to specific robotic tasks. This approach addresses the data scarcity problem by providing a scalable and cost-effective solution for acquiring the necessary data to train advanced robotic manipulation models. The platform facilitates the collection of real-world data, allowing robots to learn from human demonstrations and improve their performance in complex environments.
Target Audience
Sensei targets robotics companies and AI research labs that require large, diverse datasets for training robotic manipulation models.
Features
- Teleoperation interface for remote control and data collection
- Network of trained human operators for diverse task execution
- Data labeling and annotation tools for high-quality training datasets
- Customizable data collection protocols tailored to specific robotic tasks
- Scalable platform to accommodate varying data volume requirements