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A research robot has to support experiments its designers haven’t thought of yet. Matt Trossen explains why we build our hardware to be changed.

A university research lab is using the Trossen Solo AI kit to teach a robot arm to pick up any manufacturing part, in any orientation, and place it in the right bin from a simple prompt. Ali Beydoun, a researcher focused on physical AI, documented the full journey in a detailed post: Bringing a Robot Arm to Life with Trossen Robotics AI. He covers how he chose an arm, set it up, collected training data and fine-tuned a vision-language-action model. He also notes that his post

Design multimodal robot data workflows that align joint states, RGB-D video, actions, timestamps, and metadata for reliable physical AI training.

Collecting handheld demonstrations is only half the job. The other half is robotics data annotation: turning hours of raw episodes into multimodal training data a team can actually search, review, and train on. Our partners at DecisionFacts just showed what that second half can look like. They built a pipeline that takes episodes captured on TRumi, Trossen Robotics' handheld data collection device, and turns them into annotated, synchronized files that open directly in Foxglo

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Evaluate a robotics workstation for physical AI with a scorecard covering GPU, CPU, memory, storage, CUDA, ROS, simulation, data, and total cost.

Learn how to evaluate robot training data for synchronized actions, metadata, task coverage, validation, bias, and readiness for physical AI policy training.

Two Trossen arms struck a match completely on their own. The demo comes from NAC, research by Ahad Jawaid and Yu Xiang at UT Dallas heading to CoRL 2026, which borrows audio compression tricks to help robots learn precise, coordinated tasks.

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.