Problem
High-variability logistics — totes tumbling, parcels sliding, parts rolling on a vibrating tray — defeat reactive vision. By the time the robot sees the object, the object has already moved.
Generative forecasting
Predictive AI
FutureVision — generative models that predict object motion.
PyTorch 2.xDiffusion-PolicyROS2 HumbleMQTTRTX A6000 (train)
Core Metrics
- Prediction horizon
- 200–600ms
- Closed-loop rate
- 60 Hz
- Pick success
- > 96%
- Compute
- Jetson Orin NX
Solution
FutureVision runs a generative motion model in a closed loop with the perception stack. S3nsei predicts object trajectory 200–600ms ahead and pre-positions the gripper.
Result
Pick success on dynamic, cluttered surfaces climbs from ~74% (reactive) to >96% (predictive). Throughput on tumble-feed lines doubles without mechanical re-tooling.
Technical Logic
How Predictive AI 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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