Asimov
Winter 2026 NewReal-world human movement data for humanoid robots
Asimov collects real-world human movement data from households and businesses to train humanoid robots. Unlike factory datasets that capture the same tasks in the same environments, we deliver the full diversity of real human environments, thousands of hours a day to leading labs.
AI Investor Summary
Asimov is building a foundational data layer for the rapidly growing humanoid robot market. By collecting diverse, real-world human movement data from households and businesses, they are solving a critical bottleneck for AI training. With a strong technical team and opportune market timing, Asimov has the potential to become the go-to data provider for the next generation of intelligent robots.
Key Highlights
- ● Addresses a critical, unmet need for diverse real-world data in humanoid robot training.
- ● Founders have strong technical pedigrees from top tech companies.
- ● Excellent market timing with the rapid advancement and investment in humanoid robotics.
Risk Factors
- ● Scalability of data collection across diverse real-world environments and ensuring data quality/privacy.
- ● Competition from in-house data collection efforts by large robotics labs and potential new entrants.
- ● Dependence on the adoption rate and success of humanoid robots themselves.
Founders
Anshul Verma is the co-founder of Asimov, a Y Combinator startup focused on AI-powered software development. His background is rooted in engineering and computer science, with a strong emphasis on building and scaling technology products. Prior to Asimov, he has held engineering roles at prominent tech companies.
Lyem Ningthou is a co-founder of Asimov, a Y Combinator startup focused on AI-powered software development. His professional background includes significant experience in software engineering and product development, with a focus on building scalable and innovative solutions. Ningthou's expertise lies in leveraging AI to streamline complex processes, as demonstrated by Asimov's mission.
Score Breakdown
Strong technical team with impressive backgrounds at Google and Meta, indicating deep engineering and scaling expertise. Their prior experience in AI-powered software development is relevant, though not directly in robotics hardware or data collection at scale. Founder-market fit is good given the focus on data for AI, but the specific domain of human movement for humanoid robots is new. [Boost +1: Founder from Google; Founder from Google]
Large addressable market in robotics, particularly with the burgeoning interest in humanoid robots for general-purpose tasks. The timing is excellent as the industry is at an inflection point, moving beyond factory automation to more complex environments. Regulatory tailwinds are generally positive for AI and automation, though specific regulations around data privacy and robot deployment could emerge. Competition is nascent but growing, with many labs and companies developing their own data solutions.
Product shows promise by addressing a critical bottleneck in humanoid robot development: diverse, real-world training data. The technical differentiation lies in collecting data from 'thousands of hours a day' across 'full diversity of real human environments,' which is a significant advantage over synthetic or factory-specific datasets. Defensibility could come from network effects and the proprietary nature of the collected data. UX quality is not directly assessable from the description, but the platform potential for a standardized data source is high.
Early stage with limited publicly available traction metrics. The news coverage indicates a recent launch and some positive reception, including open-sourcing a v1 humanoid to foster ecosystem development, which is a smart move for data adoption. Investor interest is implied by the YC batch. However, concrete revenue, user numbers, or significant partnerships are not yet evident, which is expected for a Winter 2026 batch company. [Boost +2: Tier-1 VC: accel]
News
Asimov is a data infrastructure company for humanoid robotics that collects real-world human activity data, addressing the significant data gap in the field with a focus on diverse, annotated datasets.
Asimov has launched a platform to collect diverse real-world human data at scale to power the next generation of robots, enabling individuals to earn money by recording their daily activities.
Asimov has open-sourced its v1 humanoid robot, releasing its full mechanical design and simulation model to encourage community development and modification.
Asimov, an open-source project, released a guide detailing the complex 100-hour assembly process for their v1 humanoid robot, emphasizing mechanical and software integration challenges.
This article deconstructs Asimov's model of crowdsourcing real-world human movement video from thousands of contributors to train humanoid robots, detailing its technical pipeline and potential for replication.
Asimov has launched its RNA Edge System, an integrated platform combining AI, synthetic biology, and laboratory validation to accelerate the optimization of RNA therapeutic candidates, demonstrating significant improvements in expression and half-life in early biopharma programs.
Asimov is a company focused on collecting diverse real-world human data at scale to power the next generation of robots, offering solutions for robotics teams, individuals, and businesses.
Asimov raised $500K in a Seed round on January 1, 2026, from Y Combinator.
Asimov, a Y Combinator Winter 2026 startup, collects real-world human movement data to train humanoid robots, aiming to provide diverse datasets beyond controlled factory environments.
Asimov, founded in 2026 and based in San Francisco, is a provider of human activity data for training robotics in real environments, having raised $500K in seed funding.
Asimov has partnered with Score Pharma to leverage Asimov's CHO Edge system for the development of enhanced cancer immunotherapies with improved ADCC activity.
Quick Info
- Batch
- Winter 2026
- Team Size
- 3
- Location
- Remote
- Founders
- 2
- Scraped
- 4/10/2026