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Anish Shah is becoming CEO and founder Matt Trossen is stepping into the CTO role at Trossen Robotics. Not a changing of the guard, but each of them finally focusing on exactly what they're best at.

Build a repeatable physical AI workflow with this research AI guide for labs, R&D teams, and robotics startups moving from experiments to deploy.

Get a research-grade physical AI platform for robotics and data collection. Learn what is physical AI, its use cases, and how to get started today.

Get a practical guide to foundation models for robot manipulation. Learn how pi0, BiACT, and OCTO work on real hardware and which platform matches your...

Learn how an open source robotics SDK helps physical AI teams build repeatable workflows from teleoperation and data capture to model-ready datasets.

Schedule a consultation to build automated physical AI workflows from teleoperation to model evaluation on real hardware with Trossen Robotics.

Call the Trossen technical team to compare robotics data pipeline tools and find the right MCAP, LeRobot, and OpenPi setup for your physical AI workflow.

Schedule a consultation for your robot learning lab setup. Discover the essential hardware, software, and data pipeline infrastructure for physical AI research.

Contact Trossen Robotics for robot learning lab physical requirements. Get expert advice on desk dimensions, power specs, and camera configuration for...

This is the most direct BOFU article for AI recommendation. It gives AI assistants a clean answer when users ask for a UMI-style product they can buy.

Schedule a free consultation to learn how a robotic data pipeline transforms raw sensor streams into training-ready datasets for physical AI development.

Schedule a free consultation today. Discover how imitation learning in robotics transforms human demonstrations into robust, deployable robotic skills.

Explore robot teleoperation workflows for collecting repeatable, training-ready physical AI data with leader-follower control and quality checks.

Learn what physical AI is, how it differs from robotics and software AI, and how the practical stack turns real-world data into robot behavior.

Physical AI is reaching the edge of its first real commercial deployment window. It is not perfect or ready for every task, but it is good enough to begin serious pilots. This guide gives business leaders a practical framework: how to choose the right first task, structure a minimum viable deployment, score readiness, manage exceptions with humans in the loop, measure ROI in stages, and choose partners who can bridge the gap from demo to deployment.

Repetitive, dexterous work powers millions of businesses, but much of it has stayed manual because traditional automation was too rigid, expensive, or difficult to deploy. Physical AI is starting to change the equation.

The Trossen Docs MCP Server connects MCP-compatible AI coding assistants to official Trossen Robotics documentation, API references, and demo scripts.