* Not including the weight of the table edge fixing clamp (290±10g).

supports the popular Hugging Face LeRobot AI robotics project,
comes with stable and regularly updated demos
options for Jetson Orin Nano Super / Jetson Orin NX Super development board

The RoArm-M3 robotic arm series supports the open-source LeRobot framework on Hugging Face, providing core resources such as pretrained models, datasets with human collected demonstrations, and simulation environments. When combined with the N-VIDIA Jetson Orin Nano Super / Jetson Orin NX Super motherboard or a PC with an N-VIDIA discrete GPU, users can experience the complete process of Imitation Learning and Reinforcement Learning projects.

adopts Leader-Follower robotic arm solution to collect data and build datasets for subsequent model training in imitation learning and reinforcement learning

Adopts 360° rotation base and flexible joints to create an omnidirectional operating space with 1-meter diameter, allowing the Robotic Arm to move freely in all directions

Equipped with 2 DOF wrist joint with pitch + horizontal rotation support, enables seamless coordination with the EoAT for multi-dimensional clamping and operation

No App installation required, access after entering the address. Allows users to connect and control RoArm-M3 via mobile phones, tablets and computers by clicking the buttons on the Web App. Supports secondary development of this open-source Web App to customize the user interface and add new functions.

Completely open source for the control codes and communication interface documents of RoArm-M3 series, supports multiple languages and devices for secondary development. Provides modularized demos and tutorials for zero-based users, easy to get started.

The joint angle feedback can be obtained directly via the 12-bit high-precision magnetic encoder without any reduction groups, which is more accurate, and the actual position of the current target point can be calculated based on the joint angle feedback.

We have developed a dual-drive control algorithm that allows the two servos at the shoulder joint to coordinate their output torque effectively, significantly enhancing the power and the overall load capacity of RoArm-M3 Series

The direct-drive clamp design allows precise control of the clamping force in the program, suitable for clamping delicate objects without applying excessive force

RoArm-M3 can save JSON instructions for robot control as task files. The task files can be saved in the Flash of ESP32, which will not be lost in case of power loss. The robotic arm can perform complex and repetitive operations by calling these task files.

ESP-NOW is a low-delay ad hoc wireless communication protocol, without the need of Network infrastructures. You can use one robotic arm to wirelessly control other robotic arms, and the control methods can be set as broadcast control, group control, and one-to-one control.

* Note: The broadcast control has no limitation for the number of controlled devices, while the group control allows up to 20 devices. Please refer to the WIKI for more details.

In the Leading-following control mode, the Leader robotic arm will send its joint angle information to other robotic arms via ESP-NOW communication. The other robotic arms which are in ESP-NOW Follower mode will imitate the same actions as the Leader in real time.

RoArm-M3 series can be controlled not only by the WEB application, but also supports using other controllers to send JSON commands via HTTP protocol network request, serial communication, or USB communication for controlling and obtaining feedback.

Comes with a camera holder and open source for the related drawings. You can directly install a camera with the corresponding size or other cameras with an adapter

* for reference only, only the LED light is included.

Adopts ESP32 powerful main control MCU, supports Multiple wireless communication protocols, with a large amount of open source resources to help secondary development of innovative applications

Provides communication nodes of ROS2, URDF model description file and zero-based tutorial of ROS2

We provide complete unit tutorials and cases, including various functions and common application scenarios, to help users get started quickly for secondary development

Integrated with INA219 battery voltage Monitoring and 9-axis IMU sensor, supports function expansion and innovation

Provides Web applications and desktop software (open-source programs developed by Python), supports setting action instructions when the mouse is pressed and released to control the robotic arm flexibly