Warehousing & Logistics
Picking, sorting, packing — and the data to make learning-based systems work at scale.
Industry Context
E-commerce and fulfillment demand ever-higher throughput and SKU diversity. Fixed automation struggles with variation; learning-based systems can generalize across items, bins, and layouts — but they need large, diverse datasets. Your warehouse has unique geometry, lighting, and product mix; off-the-shelf datasets rarely match.
What We Offer
- Data Collection — We collect pick-place, sort, and pack demonstrations tailored to your SKU mix, bin types, and workflows. Language labels for "pick the red box" style conditioning.
- W1 Mobile Manipulator — Wheeled base + arm for mobile picking. Navigate aisles, reach shelves, return to stations.
- Fearless Data Platform — Log pick failures, mis-grasps, and wrong-bin errors. Replay, analyze, and retrain. A/B test policy updates before rollout.
- RL Environment — Persistent real-world environments for training and evaluating pick policies without blocking production lines.
Value We Deliver
- Generalization — Policies that handle new SKUs, cluttered bins, and varying poses without manual reprogramming.
- Throughput — Faster cycle times via learned recovery, batching, and multi-step sequencing.
- Lower integration cost — Learning reduces the need for precise fixturing and rigid workflows.
- Continuous improvement — Platform closes the loop: failures become training data.
Example Use Cases
- Order picking: cartons, totes, individual items
- Sortation: divert, palletize, depalletize
- Returns processing: inspect, sort, restock
- Kitting and assembly for custom orders