[ APPROACH ]
Pre-train. Fine-tune. Deploy
[ LEARNING ]
Improves through production data
Failure Detection
A separate ML model detects when the robot fails or hits something it hasn't seen
Data Capture
Every edge case gets logged and added to the training set.
Retraining
Models are periodically retrained on edge-case data to improve task success rates.
OTA Updates
Updated models are deployed remotely as part of scheduled updates.
[ HARDWARE ]
Hardware is designed for flexibility and ease of maintenance
Modular Design
Arms, grippers, and payload configured to your workstation
Serviceable Parts
Widely available components. Lower mean-time-to-repair.
Dual-arm System
Two arms for flexible parts, cable handling, kitting, and flexible part manipulation
See the robot in action
96.77%
Placement success rate
54.2s/part
Achieved cycle time
4
Auto-recovered failures
[ Hardware specs ]