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【Embodied Al Robotic Arm】NexArm robot arm features an industrial-grade metal body with high-precision magnetic encoder servos and inverse kinematics. It delivers a 500mm reach,500g payload, and +2mm repeatability. Curve smoothing algorithms eliminate jitter for precise grasps and smooth trajectories.

【Compatibility with the LeRobot Ecosystem & End-to-End Algorithms】NexArm robotic arm is fully integrated with the LeRobot framework to access community models, datasets, and simulations. Developers can easily train and deploy end-to-end imitation and reinforcement learning algorithms like ACT.

【Leader-Follower Teleoperation & VLA Development】Supports synchronous teleoperation via leader and follower arms. By capturing HD video alongside trajectory data, NexArm robotic arm quickly builds "vision-action" datasets, making it an ideal platform for VLA (Vision-Language-Action) model training.

【Dual-Camera Vision System】Equipped with both a gripper-mounted camera and an external camera, the robot arm system supports both precise manipulation and environmental awareness for accurate imitation learning.

【Open-Source and Multi-Mode Control】Open-source code and schematics, plus PC software, app control, secondary development examples, and structured tutorials, significantly lower the barrier to embodied AI development.

【Raspberry Pi 5 & ROS2 Robot Car】 MentorPi M1 robot, powered by a RPi 5 and compatible with ROS2, is programmed in Python, making it an excellent platform for developing AI robots.

【Built for Performance】 With closed-loop encoder motors, a TOF lidar, a 2DOF camera, and high-torque servos, the MentorPi car ensures optimal performance and efficiency for all your robotics projects.

【Unlock Advanced AI】 Explore a wide range of AI applications with features that include SLAM mapping, path planning, multi-robot coordination, vision recognition, and target tracking.

【Learn Autonomous Driving】 Utilize the YOLOv11 model for training the robot to recognize road signs and traffic lights, helping you explore and develop cutting-edge autonomous driving technologies.

【Arduino Programming, Open Source】Spiderbot is compatible with Arduino programming. The programs for Spiderbot are open-source, and learning tutorials and secondary development examples are available, making it easier for you to develop your robot.

【High Performance, Support Sensor Expansion】 Spiderbot is equipped with a glowy ultrasonic sensor, a Dot Matrix Module, intelligent bus servos, and other high-performance hardware. Moreover, it provides multiple expansion ports for sensor integration, including accelerometer, touch sensor, sound sensor, etc., empowering users to engage in secondary development.

【Versatile Control Options】Spiderbot supports pc software control, app control and wireless handle control.

【Next-Gen Embodied AI Robotic Arm】Built on an ESP32 + AT32 dual-chip architecture, with an industrial-grade all-metal body and advanced inverse kinematics algorithms, NexArm delivers a full-stack solution from low-level hardware to end-to-end large-model validation.

【Compatibility with LeRobot Ecosystem & End-to-End VLA Models】NexArm robotic arm is fully integrated with the LeRobot framework to access community models, datasets, and simulations. Developers can easily train and deploy end-to-end imitation learning algorithms and multimodal models like ACT & VLA.

【65 kg·cm High-Torque Magnetic Encoder Bus Servos】Industrial-grade high-precision magnetic encoder bus servos, combined with trapezoidal acceleration/deceleration and curve-smoothing algorithms, reduce start-stop jitter, enable smooth motion, and deliver ±2 mm repeatability.

【6 TOPS K230 Vision Module & Al Voice Interaction】NexArm robot arm equipped with the K230 Al vision module, with 30+built-in Al vision features including color/objects/gesture recognition and sorting, personalized face recognition, and more. Supports voice control, Al vision & voice interaction, and hand-eye coordinated grasping.

【Open-Source and Multi-Mode Control】Open-source code and schematics, plus PC software, app control, secondary development examples, and structured tutorials, significantly lower the barrier to embodied AI development.