- Specialization
- Robotics
- Business
Data Provisionand 2 more business valuesLess
- Hardware Manufacturing
- Robotics Systems
- Market
Manufacturingand 1 more market valuesLess
- Software Development
- Ecosystem
- Not classified
- Customers
Enterprisesand 1 more customers valuesLess
- Researchers
More details
- Organization type
- Commercial Company
- Ecosystem
- Not classified
- Business
- Data Provision, Hardware Manufacturing, Robotics Systems
- Specialization
- Robotics
- Market
- Manufacturing, Software Development
- Customers
- Enterprises, Researchers
- Delivery models
- Professional Services, Hardware
- Revenue models
- Not classified
- Listing status
- Not classified
- Latest equity round
- Not classified
- Reported funding amount
- Not classified
About
Sensei Robotics builds hardware and software for collecting human-demonstration and in-the-wild robotics training data. It sells to robotics companies and differentiates itself through a paid operator network and collection hardware that YC says is about twice as fast and one-tenth the cost of current teleoperation approaches.
Market
Sensei competes in the emerging robotic training-data and physical-AI infrastructure market, positioning itself as “Scale AI for robotics” by combining data-collection hardware, workflow software, and a distributed labor network. Its main differentiation is a sub-$300 collection platform designed to make human-demonstration data collection substantially cheaper and faster, while enabling diverse in-the-wild data rather than relying only on expensive lab-based teleoperation fleets.
Robotics companies and robotics AI labs—particularly teams developing manipulation, humanoid, or other physical-AI systems—that need large volumes of real-world human-demonstration data. The likely buyers are robotics researchers, engineers, and AI/data leaders who want to outsource collection rather than build and operate expensive fleets of teleoperation setups.
At a Glance
Problem
Robotics companies need large volumes of high-quality human-demonstration data to train manipulation and embodied-AI systems, but data collection remains a major bottleneck. The conventional approach uses fleets of 5–20 in-person teleoperators and requires a physical robot—often costing more than $40,000 per operational platform. Those setups are expensive, slow, difficult to scale, and tend to produce less varied data because collection is confined to labs or offices.
The clearest use cases are everyday manipulation tasks such as clothes folding, bin sorting, and dishwasher loading. These tasks require demonstrations across many environments and human strategies, but cumbersome teleoperation equipment and unreliable hardware limit collection speed and make it uneconomic to generate training data at the scale needed for capable robots.
Product / Service
Sensei combines low-cost data-collection hardware with a software-enabled operator network. Its platform uses a sensorized exoskeleton arm equipped with angle, vision, and inertial sensors to capture accurate visual and spatial information while a person performs a task. The hardware is designed to be portable, intuitive, and usable outside a specialized robotics lab; Sensei says the platform costs less than $300.
On the service side, robotics companies submit data requests, and a distributed network of trained, paid “Senseis” equips the platform and collects demonstrations in varied real-world settings. Sensei positions this combined hardware-and-marketplace model as roughly one-tenth the cost and twice the speed of existing teleoperation approaches, while giving customers access to more diverse training data without operating their own collection fleet.
Market
Sensei operates in robotic training-data infrastructure and human-in-the-loop data collection for embodied AI, rather than selling autonomous robots itself. Its model sits between robotics engineering services, data-labeling marketplaces, and teleoperation-data providers. Adjacent competitors identified in company databases include Scale AI, Sama, and Toloka, all of which offer broader or specialized data-collection and annotation services relevant to robotics.
The company was founded in 2024, participated in Y Combinator’s Summer 2024 batch, and is listed as active. Public market profiles describe the product as being in beta, while the company’s own site invites customers to request data and operators to join its beta. The available materials do not identify named customers, production deployments, or verified revenue, so the strongest evidence points to an early-stage, pre-commercial or early-commercial company with a promising cost and speed proposition rather than demonstrated large-scale traction.
Founders & Leadership
Funding History
Y Combinator
Recent News
The Google Play listing describes Sensei’s app for recording everyday tasks such as washing dishes or folding laundry and earning an hourly rate for submitted videos.
The integration page says Sensei is compatible with CB3, e-Series, and UR Series robots running PolyScope 5.13+, and can operate as a PolyScope program node.
A funding profile reports that Sensei has raised $23.1 million across three rounds, including a $16.0 million Series A in October 2024. The source is recent coverage of the company’s financing history rather than evidence of a new round during the review period.
Active Roles
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Get notified when they postBusiness Model
Sensei appears to monetize B2B robotics data-generation services and platform usage: robotics researchers and engineers submit task descriptions, while paid operators collect the requested demonstrations. Public materials describe the workflow but do not disclose a formal customer pricing schedule.