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Get a free consultation to compare bimanual vs single-arm robot manipulation platforms for your physical AI lab. Find the right robot system today.

Get a quote and compare mobile ALOHA vs stationary ALOHA for bimanual manipulation, data collection, mobility, computing, cameras, and research workflows.

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

Most Physical AI projects don’t fail in demos. They struggle later, when systems weren’t set up for the work they’re asked to do. At Trossen Robotics, we partner with teams early to stand up scalable hardware, software, and data systems that hold up under real use. By addressing wear, embodiment fit, and data scale upfront, we help teams reduce friction, adapt over time, and maintain momentum as projects grow.

Sitcoms don’t succeed because of one great episode. They succeed because the world they build can keep generating new stories. Physical AI faces the same test. As programs mature, platforms must carry learning forward across time, people, and projects. From serviceable hardware and expressive kinematics to intuitive teleoperation, coherent SDKs, and data continuity, we design physical AI systems to support long arcs of real work—not one-off demos.

Our support inbox isn’t just troubleshooting. It’s a window into how real teams succeed with robotics. Every question reveals how data practices, intuitive teleoperation, system stability, and clear integration paths shape progress in the real world. By staying close to support conversations, we’re learning what helps teams move faster, stay confident, and build momentum—and using those insights to design tools that scale with real work, not demos.

Trossen AI robotic arms are now fully integrated with the OpenPI framework, enabling data collection, training, and inference of state-of-the-art VLA models like π₀ and π₀.₅. Developed by Physical Intelligence, OpenPI supports scalable, general-purpose robotics using vision-language-action learning. With this integration, users can fine-tune and deploy robotic policies on real hardware. Docs now live.

How to stretch your runway, accelerate milestones, and avoid the hidden costs of the wrong hardware partner.

The Trossen TOTL Workstation delivers top-tier performance, Linux compatibility, and preloaded AI tools—designed for machine learning without the enterprise price tag.

Trossen Legacy Hardware Reaches End-of-Life.

Trossen Robotics and General Robotics Announce Partnership to Bring AI-Enhanced Capabilities to Trossen’s AI Robot Lineup via GRID

View the new features, changes, and bug fixes for the latest 1.8.1 firmware for Trossen AI.

Trossen Robotics hardware featured in DeepMind’s AI research on CBS’s 60 Minutes, highlighting our role in advancing embodied AI and machine learning.

The Short Version Answer five scoping questions on data, training location, environment, mobility, and bimanual needs. Match your project to Stationary AI, Mobile AI, Solo AI, or modular WidowX AI arms. Size compute by task: light CPU for data collection, 8GB+ VRAM GPU for training, dedicated GPU for Pi0. Pick preloaded compute from Dell, System76, or ASUS, or bring your own meeting minimum requirements. Confirm your kit ships with leader/follower arms, Intel RealSense D405 c

WidowX AI Unboxing and Assembly Video Guides

Learn how we have evolved Aloha to deliver more performance and more value than ever.

The Short Version Run zero-shot inference on Pi Zero (π0) using a Trossen Robotics Aloha Kit to verify real-world transfer. Provision a workstation with a 12th Gen Intel Core i9-12950HX, NVIDIA RTX A4500 16G, and 64G RAM. Install Ubuntu 22.04 and the required dependencies: PyTorch, CUDA, and Docker. Clone the official Pi Zero repository and apply the system optimizations and adjustments for Aloha. Deploy on the bimanual Aloha platform and execute dexterous tasks without addit

The ALOHA 2.0 package brings a transformative update to the world of robotics, introducing modular configurations and enhanced flexibility.

Fireside chat with Remi Cadene, from Hugging Face, and Shantanu Parab, from Trossen Robotics.

The Short Version Launch the LeRobot Colab Notebook and select your GPU type (A100 or T4). Confirm you have enough compute units, and load a checkpoint file if resuming. Log in with your Hugging Face token to access Trossen Robotics Community datasets or your own repo ID. Edit the YAML config to set batch size, learning rate, and training steps (we used 50 episodes, batch size 8, 80,000 steps). Estimate resources by timing the first steps and checking the usage rate in the Re