Anthony Cantu

**LearnLoop — Teach a task once. Verify it again.** LearnLoop is an AI-powered application that learns physical procedures from video demonstrations and evaluates whether those procedures were completed correctly in subsequent recordings. Today, verifying hands-on tasks often requires manual supervision or expensive, task-specific computer vision systems. LearnLoop explores a more accessible alternative: demonstrate a procedure once, review what the system learned, and use it to check future attempts. **How it works:** 1. **Teach:** Upload a video demonstrating a task. Meta's Muse Wonder identifies objects, actions, and completion conditions, while SAM 3.1 tracks objects and DINOv2 builds visual appearance references. 2. **Review:** LearnLoop generates an editable, step-by-step procedure. Users can correct object labels, adjust requirements, establish ordering constraints, and approve the final procedure. 3. **Verify:** Upload a separate recording. LearnLoop independently analyzes the attempt and uses deterministic logic to compare observed evidence against the approved requirements. 4. **Inspect:** Receive step-level PASS, FAIL, CANNOT VERIFY, or NOT APPLICABLE results, with timestamps, visual evidence, and synchronized demonstration-versus-attempt playback. Unlike systems that require an identical sequence of movements, LearnLoop can recognize successful completion even when steps occur in a different order, provided the procedure doesn't require a specific sequence. When evidence is ambiguous, LearnLoop reports uncertainty rather than guessing. Targeted AI reinspection and human-confirmed observations provide additional ways to resolve inconclusive results. **Demonstrated results:** LearnLoop has been tested using real fixed-camera and head-mounted recordings, including multi-step LEGO assembly and hardware tasks. In one demonstration, it learned a five-step motorcycle assembly procedure and correctly identified a missing component in a subsequent attempt. Development testing included 20 analysis runs, 16 verifications, and 75 live Meta API requests. A built-in Reliability Lab also enables repeated independent analyses to investigate consistency and model variability. LearnLoop demonstrates a path toward accessible, reusable visual verification for hands-on training, assembly, quality assurance, and other physical workflows—without requiring custom model training for each procedure.