1. A connected workspace for software development
Booster Studio brings application development, simulation and debugging into one desktop environment. Developers can inspect camera feeds, visualise robot data and review motion alongside their application code.
AI-assisted coding helps turn development instructions into code for review and testing. Support for Windows, macOS and Linux gives teams flexibility in their choice of development workstation.
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2. Free, open-source training and deployment tools
Booster’s open-source training framework includes support for T2, giving researchers a starting point for reinforcement learning and motion-tracking development in Isaac Lab.
The accompanying deployment framework supports testing trained policies in MuJoCo and running them on physical Booster robots. Both projects are available to download for free, with source code that teams can inspect and adapt under their published licences.
Explore Booster’s open-source software
3. Flexible access through Python, C++ and ROS 2
Booster provides Python and C++ development interfaces alongside ROS 2 integration resources.
Developers can read sensor and joint data, issue movement commands and build applications around the robot’s capabilities. This gives teams a choice between application-level programming and more detailed control work, depending on the project.
4. Whole-body movement and configurable hands
T2 stands approximately 140 cm tall, weighs around 42 kg and has 31 degrees of freedom across its body. Seven degrees of freedom in each arm support manipulation research, while an articulated waist adds flexibility to whole-body movement.
Optional grippers and dexterous hands allow the configuration to reflect the objects, reach and handling tasks being investigated.
5. Onboard AI and multiple camera viewpoints
Head and waist cameras provide visual inputs for perception development, with additional wrist or end-of-arm cameras supported on relevant handling configurations.
The Professional edition includes NVIDIA Jetson Thor computing for demanding onboard AI workloads. Teams can select hardware around their models, sensor requirements and intended applications.