Problem
Traditional vision pipelines need thousands of labeled samples per SKU. Reflective metal, transparent polymer, and high-mix low-volume lines never generate enough data to train.
Foundation perception
Object Intelligence
Zero-Training Perception for reflective and complex parts.
ONNX RuntimeTensorRT 10CUDA 12Jetson NanoOpenCV 4.x
Core Metrics
- Setup time per SKU
- < 5 min
- Training samples
- 0
- Pose accuracy
- ± 0.2mm
- Material support
- metal · glass · poly
Solution
The S3nsei Edge Kit ships a foundation perception stack on Jetson Nano. Detect, classify, and estimate 6-DoF pose for novel parts on day one — no model fine-tuning, no labeled dataset.
Result
New SKUs go live in minutes. Reflective brackets, chrome trim, and translucent caps are handled out of the box. Operators add parts via a single reference photo.
Technical Logic
How Object Intelligence runs on the edge.
A live trace from a production unit. Single binary, deterministic latency, every dispatch logged locally.
Chennai R&D · Robots Learning Lab
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