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Explore mobile robot requirements, mobility options, sensing, data workflows, and validation steps to scope a platform for physical AI research.

Explore tactile sensing and control of robotic manipulation, from contact detection and force regulation to data capture, evaluation, and sim-to-real tests.

Learn how to choose a robot servo controller by comparing communication, feedback, torque, timing, safety, and software needs for your robotics project.

Open X Embodiment explained: explore its scale, robot diversity, research value, data formats, limitations, and path to real-robot evaluation.

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.

Choose robotic end of arm tooling by task, payload, reach, interfaces, and repeatability with this practical guide for research and automation teams.

Evaluate a robot data collection platform across hardware, teleoperation, capture, metadata, storage, quality checks, and scale before you commit.

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

Explore behavior cloning robotics, from recording clean teleoperation demonstrations to validating learned policies and deploying them on real hardware.

Compare machine learning robot arm platforms across hardware, cameras, teleoperation, data, compute, inference, documentation, and scale for physical AI.

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

Evaluate a bimanual robot arm for research with a practical framework for synchronization, reach, calibration, sensing, software, and support.

Explore physical ai startup decisions for hardware, software, data workflows, and support, from first prototype through a repeatable pilot.

Learn how to integrate a robot arm ROS workflow for research, then build a repeatable path from motion and sensing to safe, synchronized data capture.

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.

Learn how to capture robotics training data with better task design, teleoperation, synchronized sensors, episode metadata, quality control, and evaluation.

Learn how bimanual manipulation teams coordinate arms, sensing, teleoperation, calibration, data capture, and evaluation for repeatable robot-learning research.

Explore the WidowX robot arm's specifications, sensing, configurations, and software, then assess its fit for university, startup, and enterprise R&D workflows.

A mobile aloha robot field guide for research teams, covering bimanual workflows, teleoperation, repeatable demonstrations, data capture, and model evaluation.

Learn robot arm calibration for research workflows, with guidance on joint zeroing, kinematics, TCP, camera alignment, validation, and data collection.