Nearfield Robotics

Nearfield is building readiness evaluation infrastructure for robots entering shared human spaces. Robotics progress is still judged too often by highlight clips, private QA notes, and one-off field demos. Nearfield turns robot episodes into structured evidence: scenario, risk, score, reviewer signal, adapter output, and deployment verdict. The project is not a robot manufacturer and does not replace robot controllers. It sits after robot execution as a proof and evaluation layer. A robot team, operator, or contributor can submit evidence from a real clip, simulation rollout, telemetry trace, or staged test, then receive a readiness report that explains whether the episode is suitable for the target scene and where it needs improvement. The first product wedge is narrow: shared-space robot readiness. This includes corridor crossing, lobby reception, queue merge, object transfer, indoor navigation, calibration gates, and other episodes where task completion alone is not enough for deployment confidence. *** ## 2. Introduction Robots are moving from controlled demonstrations into environments shared with people: hotels, hospitals, warehouses, campuses, retail spaces, offices, and public facilities. The hard question is no longer only whether a robot can complete a task. The harder deployment question is whether the episode is ready for the scene. A robot may avoid collision and still fail the deployment test. It may enter a corridor without enough visible intent. It may complete a delivery while creating uncertainty around nearby people. It may pass a technical benchmark while producing a poor operator review. Today, many of these signals remain scattered across videos, logs, screenshots, internal notes, and subjective comments. Nearfield turns those fragments into an evaluation protocol. The system ingests robot evidence, segments the episode, applies scenario-specific scoring, captures reviewer judgment, and generates a readiness report. Over time, reports create a benchmark graph across robot classes, scene types, runtime versions, contributors, reviewers, and deployment outcomes. Nearfield's long-term ambition is to become the proof layer between robotics demos and real-world deployment. *** ## 3. Why This Category Is Bullish Physical AI is entering a new phase. More teams can produce impressive robot videos, simulation rollouts, teleoperation clips, and early prototypes. But the market still lacks a shared way to judge whether these systems are deployable in human-facing environments. That creates a gap between attention and adoption. Investors, operators, communities, and robot teams need a way to evaluate real progress beyond the best clip. They need evidence that can be compared across time, across scenarios, and across robot updates. Nearfield is focused on that missing layer. Several trends make this category timely: * Robot hardware and embodied AI models are improving, which increases the need for deployment evaluation. * Service robots, humanoids, delivery robots, inspection robots, and warehouse robots are moving closer to human environments. * Simulation and video data are becoming more available, but raw media still needs structured interpretation. * Communities around physical AI need better ways to contribute useful evidence instead of only reacting to demos. * Token networks can coordinate contribution, review, access, and reputation around evaluation work. Nearfield is not betting on one robot body. It is betting on the growth of the entire robotics deployment surface. *** ## 4. Problem Statement Robotics has a proof problem. Current evaluation often falls into four weak patterns: 1. **Highlight-clip evaluation** The best video is treated as evidence, while failed attempts, ambiguous encounters, operator interventions, and reviewer disagreement disappear. 2. **Internal-only QA** Robot teams may have private testing processes, but outside reviewers, partners, communities, and future contributors cannot inspect or compare them. 3. **Task-only success metrics** Completing the task is necessary but not sufficient. Deployment also depends on legibility, timing margin, comfort geometry, scene fit, recovery quality, and perceived trust. 4. **Unstructured human judgment** Human review is valuable, but without rubrics, timestamps, confidence levels, and reviewer calibration, it cannot become reusable infrastructure. Nearfield addresses this by converting robot episodes into auditable readiness reports.