Field Notes — Simovian Intelligence

Research, analysis, and field notes on embodied AI, robotics, and regulated training data, written by the team building contact-rich care-setting datasets for safer physical AI. We publish the thinking behind our capture decisions, our reads on the broader robotics-data landscape, and original analysis of where the field is heading.

Topics we cover

• The training-data bottleneck for affordable humanoid robots and what closes it.
• Vision-language-action (VLA) model evaluation: where current benchmarks succeed and where they mislead.
• Consent, compliance, and the data-rights layer for embodied AI in regulated environments.
• Real-world demonstration capture: cost structures, throughput, and what scales.
• Generalist policy transfer from lab to deployment, and the long-tail data the field is missing.
• Human-AI orchestration of physical tasks — what changes when an LLM coordinates a human worker.

Why we publish

We write in public about what we learn because the embodied AI data space is young and poorly understood. Most teams evaluating training data vendors have no benchmark for what good looks like — what annotation depth matters, what capture methodology produces usable demonstrations, how consent protocols affect data utility. By publishing our analysis, we give potential partners a way to evaluate our thinking before any commercial conversation starts. We would rather be judged on the substance of our reasoning than on a pitch deck.

Every post reflects real decisions we are making about capture methodology, annotation design, or market positioning. We do not publish generic industry overviews or speculative think-pieces. If we write about a topic, it is because we have a stake in getting the answer right.

Who this is for

• Robotics engineers evaluating training data sources. If you are deciding between self-collection, synthetic data, teleoperation datasets, and third-party real-world data, these posts lay out the tradeoffs we see from the data-production side.
• Embodied AI researchers tracking the field. We cover emerging model architectures, benchmark gaps, and the data requirements behind headline-grabbing demo results — with an emphasis on what changes when you move from lab to deployment.
• Investors doing diligence on physical intelligence and robotics data. Our analysis of market structure, data scarcity, and defensibility in this space is written for people making allocation decisions, not just following the news.
• Policy and compliance teams in healthcare robotics. Consent, data rights, and regulatory readiness are recurring themes. We write about these topics from the perspective of a team that operates in regulated environments daily.

How we think about the data landscape

Several convictions run through our writing. Real-world demonstration data produces more robust policies than simulation alone — the gap widens as tasks involve deformable objects, human cooperation, and unstructured environments. Annotation quality matters more than dataset volume: a smaller dataset with clinically validated sub-task labels trains better policies than a massive corpus with noisy crowdsourced annotations. Consent-first data collection is a competitive advantage, not a compliance burden — teams that build on defensible data avoid the legal and reputational risks that will eventually catch up to those that do not. And the gap between demo performance and deployment performance is almost always a data problem, not a model problem.

Reading recommendations

• New to embodied AI training data: Start with the humanoid robots data gap for an overview of why hardware commoditization does not solve the data bottleneck.
• Evaluating foundation models or data vendors: Read the analysis of real-world data requirements behind headline performance claims to understand what questions to ask about training data provenance.
• Interested in consent and compliance: See the consent story for home-deployed humanoids for our perspective on why data rights are the underappreciated constraint in physical AI deployment.
• Thinking about data generation at scale: Read how AI-orchestrated human workflows create training data as a byproduct for analysis of emerging capture paradigms beyond traditional teleoperation.

All posts

Subscribe via RSS · Working with us