VLA fine-tuning
Foundation models, adapted to your task
Open VLA models (GR00T N1.7, π0, OpenVLA) fine-tuned on customer data. An abstraction layer lets us swap base models without retraining your task intelligence.
Мозг ИИ для любого робота
AI Systems
POLCERO строит интеллектуальный слой ИИ - восприятие, планирование, управление и управление парком - для человекоподобных и специализированных роботов. Тот же стек ИИ обслуживает все направления POLCERO: дообученные модели VLA, зрение YOLO, архитектуру edge+cloud и маховик данных. Аппаратная часть может быть собственной у клиента или выбрана из нашего каталога; мы поставляем интеллект.
TO ORDER
<10 msEdge control latency
VLAFine-tuned foundation models
RGB-D · LiDARMulti-modal perception
Edge + CloudHybrid architecture
Capabilities
The intelligence layer
The same AI stack powers every POLCERO branch. The robot hardware can be your own or from our catalogue - we supply the brain.
Vision and perception
Real-time scene understanding
YOLO-class detection for real-time object recognition, depth cameras (RGB-D), LiDAR, multispectral and thermal imaging, plus a VLM for scene and language understanding.
Edge + cloud architecture
Sub-10 ms control, cloud-scale learning
On-board edge inference for sub-10 ms control. Cloud for training updates, analytics and fleet management. A data flywheel continuously improves performance.
Classic control where sufficient
AI only where it earns its place
PID controllers for simple, repetitive grasping and cutting - lower cost, higher reliability. We reserve AI for complex perception and scene-understanding decisions.
Fleet management
Coordinate many robots as one system
Central supervision of a mixed robot fleet: task allocation, telemetry, model rollouts and productivity reporting from a single control layer.
How we engage
From process data to a running brain
We layer intelligence onto your hardware or ours in four steps.
- 01
Data & scoping
We map the task, collect or simulate process data and define the perception and control requirements.
- 02
Model selection & fine-tuning
We pick the right VLA / vision models (or classic control) and fine-tune them on your data behind a stable abstraction layer.
- 03
Integration & edge deployment
We integrate the brain with the robot, deploy inference to the edge for low-latency control and validate against acceptance criteria.
- 04
Fleet ops & data flywheel
Cloud analytics, model updates and fleet management keep improving performance after go-live.