Trossen Stationary AI

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Unbox physical AI research with the Trossen Stationary AI — an all-in-one bimanual robotic machine learning workstation featuring two leader follower WidowX AI arm pairs, four Intel RealSense D405 cameras, and a 10″ touchscreen. Purpose-built for reproducible data collection and model training in controlled lab environments.

Unbox physical AI research with the Trossen Stationary AI — a high-performance, all-in-one robotic machine learning workstation built to meet the demand for affordable, capable data collection and model development. With a four-arm configuration, an intuitive touchscreen interface, and a high-performance desktop, it delivers cutting-edge performance right out of the box  accelerating research in automation, human-robot interaction, and advanced manipulation without the need for extensive setup or modification.

Key Features:

  • 2X Leader-Follower WidowX AI Arm Pairs – Four precision robotic arms configured for true bimanual manipulation, enabling coordinated dual-arm demonstration and data collection.
  • 4X Intel RealSense D405 Cameras – High-fidelity depth cameras positioned for consistent, repeatable multi-angle data capture across every session.
  • 1X 10″ Touchscreen Interface – Intuitive on-workstation control for streamlined data collection, monitoring, and operation.
  • Fixed-Frame Design – A dedicated frame and accessory system ensures arm placement, camera angles, and workspace geometry remain identical across every session.

Unlike field-deployable systems, the Stationary AI is purpose-built for controlled, repeatable lab environments where consistency is non-negotiable. When arm placement, camera angles, and workspace geometry remain identical across every session, the data collected is cleaner, better-structured, and more useful for training reliable imitation learning models. Researchers at universities and enterprise R&D labs choose stationary configurations because they eliminate session-to-session variation  the single biggest source of noise in physical AI datasets. This is especially critical for bimanual manipulation tasks, where left-right arm coordination requires spatial consistency that a mobile or reconfigurable system simply cannot guarantee.

Arm Configuration2x Leader-Follower WidowX AI Arm Pairs (4 arms total)
Cameras4x Intel RealSense D405
Display1x 10" Touchscreen
Setup TypeFixed-Frame (Stationary)
DeskNot included (48" x 30" recommended, maple-top surface)
Software SupportHugging Face, LeRobot, Google CoLab, Gazebo, MuJoCo, NVIDIA Isaac, ROS2, OpenPI

Description

Unbox physical AI research with the Trossen Stationary AI — a high-performance, all-in-one robotic machine learning workstation built to meet the demand for affordable, capable data collection and model development. With a four-arm configuration, an intuitive touchscreen interface, and a high-performance desktop, it delivers cutting-edge performance right out of the box  accelerating research in automation, human-robot interaction, and advanced manipulation without the need for extensive setup or modification.

Key Features:

  • 2X Leader-Follower WidowX AI Arm Pairs – Four precision robotic arms configured for true bimanual manipulation, enabling coordinated dual-arm demonstration and data collection.
  • 4X Intel RealSense D405 Cameras – High-fidelity depth cameras positioned for consistent, repeatable multi-angle data capture across every session.
  • 1X 10″ Touchscreen Interface – Intuitive on-workstation control for streamlined data collection, monitoring, and operation.
  • Fixed-Frame Design – A dedicated frame and accessory system ensures arm placement, camera angles, and workspace geometry remain identical across every session.

Unlike field-deployable systems, the Stationary AI is purpose-built for controlled, repeatable lab environments where consistency is non-negotiable. When arm placement, camera angles, and workspace geometry remain identical across every session, the data collected is cleaner, better-structured, and more useful for training reliable imitation learning models. Researchers at universities and enterprise R&D labs choose stationary configurations because they eliminate session-to-session variation  the single biggest source of noise in physical AI datasets. This is especially critical for bimanual manipulation tasks, where left-right arm coordination requires spatial consistency that a mobile or reconfigurable system simply cannot guarantee.

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