Glacier Robotics: 80,000 recyclable images labeled in 10 days to train waste-sorting robots
A high-throughput data labeling and ML verification pipeline that turned 80,000 recyclable product images into training data inside a two-week deadline. Deployed robots are now live across 10 pilot locations and two large-scale commercial sites in Northern California.
Company Background
A high-throughput data labeling and ML verification pipeline that turned 80,000 recyclable product images into training data inside a two-week deadline. Deployed robots are now live across 10 pilot locations and two large-scale commercial sites in Northern California.
Glacier Robotics needed to label 80,000 recyclable product images in two weeks to train waste-sorting robots. The work required both speed and rigor — mis-labeled training data directly degrades robotic sorting accuracy in production.
Link to ProjectEngagement
Glacier Robotics needed to label 80,000 recyclable product images in two weeks to train waste-sorting robots. The work required both speed and rigor — mis-labeled training data directly degrades robotic sorting accuracy in production.
“Their customer service has been tiers above the other partners we've worked with.”
How we approach this project
We built a streamlined data labeling workflow paired with a dedicated ML verification model that flagged suspect labels for human review before they entered the training set. Privacy-respecting handling, AWS EC2 infrastructure, and built-in QA protocols kept throughput high without letting quality slip.
The challenge
Waste-sorting robots are only as good as their training data. Glacier Robotics had 80,000 recyclable product images and two weeks to turn them into a clean, verified dataset. Throughput without quality would sabotage the robots in production; quality without throughput would miss the deadline.
What we built
- Labeling workflow. A streamlined pipeline with human labelers and structured task queues, tuned for throughput.
- ML verification model. A dedicated model reviewed labels and surfaced suspect cases for human re-examination before they entered the training set.
- QA protocols. Quality gates at multiple stages of the pipeline, not just at the end.
- Infrastructure. AWS EC2 with privacy-respecting data handling.
The outcome
All 80,000 images labeled and verified in 10 days. The dataset enabled Glacier Robotics to deploy sorting robots across 10 pilot locations and two large-scale commercial installations in Northern California.
What changed for Glacier Robotics
Delivered all 80,000 labeled images in 10 days — four days ahead of the deadline. The verified dataset enabled trash-sorting robots to deploy across 10 pilot locations and two large-scale commercial sites in Northern California.
- 01Robotics
- 02Data Labeling
- 03Computer Vision
- 04Sustainability
- 80,000
- Images labeled
- 10 days
- Delivered in
- 12 sites
- Robot deployments
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