Founder
Positronic
Positronic offers an open-source Python toolkit that streamlines the entire lifecycle of deploying AI on physical robots, covering data collection, model fine‑tuning, and production inference across any model vendor and hardware. By providing unified evaluation, inference, and data pipelines, it eliminates the engineering overhead of integrating disparate AI components, enabling reliable, scalable robot AI deployments.
- Artificial Intelligence
- Developer Tools
- Robotics
Updated 12 days ago
Funding
Funding not disclosed
Funding rounds are not available yet.
Founders
Product
Problem
Robotics teams must repeatedly integrate each new foundation model into their hardware stack, dealing with differing inference requirements, data formats, and training pipelines. This integration effort consumes weeks of engineering time and breaks when a newer model is released, preventing reliable, scalable deployment of AI on real robots.
Solution
Positronic offers an open‑source Python toolkit that unifies the entire AI‑on‑robot workflow—from data collection and teleoperation, through model fine‑tuning, to production inference. The platform abstracts hardware and vendor specifics, providing a single codebase that can run any model on any robot, whether on consumer GPUs or high‑end systems. A codec layer translates a canonical task representation into each model’s required format, ensuring fair, repeatable evaluation. Built to support the PhAIL benchmark, Positronic delivers consistent data pipelines, hardware drivers, and evaluation protocols, enabling teams to iterate quickly and maintain reliable deployments as models evolve.
Target Audience
Primary users are robotics research labs, AI‑driven automation companies, and engineering teams that need to deploy and evaluate foundation models on physical robot platforms at scale.
Features
- Unified data collection interface supporting simulation, phone, VR, and leader‑arm teleoperation
- Model‑agnostic fine‑tuning pipeline that reuses collected data across different vendors
- Standardized inference protocol that runs any VLA model on any robot hardware without custom integration
- Codec layer that maps a canonical task format to each model’s input specification for fair benchmarking
- Consistent hardware driver abstraction handling diverse GPU requirements (e.g., 78 GB VRAM, CUDA, consumer GPUs)
- Integrated evaluation suite for measuring throughput, reliability, and failure modes on real robots
- Open‑source Python library with extensible plugins for new models and robot platforms