The Panthera-HT project was born from a desire to make high-performance joint-motor robotic arms accessible to students at a lower price. Its predecessor, the Panthera robotic arm, was a 6-axis gripper arm based on DM actuators. The initial concept was proposed by wEch1ng (https://github.com/wEch1ng), who collaborated with KelvinLauMiau to share responsibilities in design, implementation, debugging, and hardware validation, ultimately completing and open-sourcing the Panthera arm. The original authors later joined forces with HighTorque Robotics, and with HighTorque's support, the project was refined and commercialized as Panthera-HT — a more complete maker product. We remain committed to open-source principles with no restrictions on the project. Precise Control, Efficient Operation Leveraging motion control algorithms and high-precision sensing, the arm achieves 0.1mm repeatability — delivering greater stability and accuracy for alignment, grasping, and continuous trajectory tasks. Outstanding Reach, Maximum Flexibility A lightweight 6-axis design combined with an 860mm extended reach delivers greater spatial coverage, allowing the arm to move freely and fluidly even in complex environments. Clean Design, Built to Last The overall design balances aesthetic consistency with structural integrity. The aluminum alloy body combines good looks, strength, and durability for long-term reliable use. Open Source, Developer Friendly Compatible with ROS2 and LeRobot, supporting C++ and Python development. URDF models and simulation support are included, making it easy to get started with research, education, and custom development. Drag joints on the model directly, or use the Joint1 to Joint6 sliders to adjust the pose. LeRobot-Ready AI Workflow From data collection to policy deployment — no extra adapter work Native LeRobot Integration Data format is natively compatible with the LeRobot/Hugging Face ecosystem, with no extra conversion needed for collection and training。 Standardized Training Data Collected data can be used directly for policy training, reducing format conversion and repeated adaptation。 Go from unboxing to a working imitation-learning policy within 24 hours, so ideas can be validated quickly。