AI Worker
Robotis

AI Worker

Semi‑humanoid robot that learns from human demos to automate complex industrial tasks, increasing productivity and relieving labor shortages.

Description

The ROBOTIS AI Worker is a state-of-the-art semi-humanoid robot platform designed for enterprise-level industrial applications, emphasizing physical AI to automate complex tasks through human demonstration and learning. Standing at 162 cm tall with models like FFW-SG2 (mobile) and FFW-BG2 (fixed base), it features 25 or 19 degrees of freedom respectively, including dual 7-DOF arms (reach 641 mm to wrist), 1-DOF grippers (RH-P12-RN, 5 kg payload), a 2-DOF head, 1-DOF lift, and optional 6-DOF swerve drive mobile base capable of 1.5 m/s velocity. Powered by NVIDIA Jetson AGX Orin 32GB (Jetpack 6.2), it integrates ROS 2 Jazzy, ros2_control at 100Hz, and Dynamixel SDK for RS-485 communication (4 Mbps) with proprietary DYNAMIXEL actuators: YM series for shoulders/elbows/lift/wheels, PH for wrists, XH for head. Architecturally, the AI Worker employs a dual-system AI pipeline: a Vision-Language Model (VLM) for semantic understanding of visual and linguistic inputs, paired with a Diffusion Transformer (System 1) for generating action chunks from robot state, enabling end-to-end vision-language-action (VLA) control. It leverages NVIDIA Isaac GR00T N1.5 foundation model, fine-tuned on 10 hours of teleoperation data (800 episodes) using 8x B200 GPUs. Deployment uses Docker containers: AI Worker for ROS2 control, Physical AI Tools for inference orchestration via ZMQ/ROS2, and GR00T for GPU-accelerated prediction (40ms latency on RTX 5090, 10 FPS control). Inputs include RGBD cameras (Stereolabs ZED Mini head: 2208x1242, 0.1-9m depth, 102° FOV, 6DoF IMU; Intel RealSense D405 hands x2: 1280x720, 7-50cm depth, 87°x58° FOV), dual LiDARs, and joint/IMU feedback. Learning emphasizes imitation from teleop/VR demos and reinforcement for refinement, supporting tasks like wiring, welding, inspection, assembly, and logistics. Real-world deployments include CoRL 2025 and Humanoid Conference 2025 demos: 85% success in sorting coffee bottles amid crowds/lighting variations (failures: 50% misclassification, 30% grasp fails, 20% phantom actions). Swerve drive enhances omnidirectionality over mecanum wheels. Fully open-source (code, URDF, sims, datasets), it runs 4+ hours on 25V 80Ah (2040Wh) LiPo battery, weighs 90kg (SG2), uses aluminum/plastic frame, operates 0-40°C. Safety via joint limits, torque control. This bridges research to industry, addressing labor shortages via scalable Physical AI.

Key Features

Physical AI Imitation Learning

Learns complex tasks from human teleoperation demos via end-to-end pipeline: data collection, visualization, training, and inference with GR00T N1.5 VLA model.

High-DOF Bimanual Manipulation

Dual 7-DOF arms with RH-P12-RN grippers (5kg payload, adaptive passive joints) enable precise handling of delicate objects like wiring and assembly.

Swerve Drive Mobility

Omnidirectional 6-DOF base (1.5 m/s) with independent wheel steering/driving for superior traction, precision, and efficiency in tight industrial spaces.

Real-Time Perception

Multi-camera suite (ZED Mini head, RealSense D405 hands) + LiDARs + IMU for SLAM, obstacle avoidance, and close-range grasping.

Open-Source Ecosystem

Full access to ROS2 code, simulations, datasets, and hardware designs for rapid R&D and customization.

ROS2 Integration

ros2_control at 100Hz for modular, real-time joint/velocity control with safety arbitration.

Specifications

AvailabilityIn production
NationalitySouth Korea
Websitehttps://ai.robotis.com/
Degrees Of Freedom, Overall25
Degrees Of Freedom, Hands7
Height [Cm]162
Manipulation Performance2
Navigation Performance2
Max Speed (Km/H)5.4
Strength [Kg]6
Weight [Kg]90
Runtime Pr Charge (Hours)4
Safe With HumansYes
Cpu/GpuAGX Orin, Nvidia Jetson
ConnectivityWi‑Fi
Operating SystemLinux, ROS 2
Llm IntegrationACT, GR00T, PI
Motor TechDYNAMIXEL
Gear TechDYNAMIXEL DRIVE (DYD)
Main Structural MaterialAluminium, Plastic
Number Of Fingers4
Main Marketlogistics, Manufacturing, R&D
VerifiedNot verified
ManufacturerRobotis
Height Cm162.3
Weight Kg90
Dof Total25
Dof Arms7 x 2
Dof Grippers1 x 2
Dof Head2
Dof Lift1
Dof Mobile6
Arm Reach Mm641
Payload Nominal Kg3 single / 6 dual
Payload Peak Kg5 single / 10 dual
Max Speed M S1.5
ProcessorsNVIDIA Jetson AGX Orin 32GB (Jetpack 6.2)
OsROS 2 Jazzy (Docker)
MotorsDYNAMIXEL-Y/P/X series (YM080-230, YM070-210, PH42-020, XH540-V150, XH430-V210); RH-P12-RN grippers
Sensorshead_camera: Stereolabs ZED Mini (2208x1242, 102°x57° FOV, 0.1-9m depth, 6DoF IMU), hand_cameras: Intel RealSense D405 x2 (1280x720, 87°x58° FOV, 7-50cm depth), lidar: x2 (models unspecified), imu: Integrated in ZED Mini and actuators
Battery25V 80Ah (2040Wh) LiPo
MaterialsAluminum, Plastic
CommEthernet/Wi-Fi 6, RS-485 @4Mbps
Control Rate100Hz (ros2_control)
Operating Temp0-40°C
Gripper Payload5kg

Curated Videos

Video 1
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Frequently Asked Questions

What AI models does the AI Worker use?

Primarily NVIDIA Isaac GR00T N1.5 VLA foundation model fine-tuned for manipulation, combined with imitation learning from teleop data and reinforcement learning. Uses VLM for semantics and Diffusion Transformer for action generation, achieving 40ms inference latency.

What is the battery life and specs?

25V, 80Ah lithium-polymer battery providing 2,040Wh capacity, supporting approximately 4 hours of continuous operation per charge in FFW-SG2 model. Includes dedicated charger; fixed base uses AC SMPS 24VDC 80A.

What sensors are integrated?

Head: Stereolabs ZED Mini (stereo RGBD, 6DoF IMU, 102° FOV, 0.1-9m depth); Hands: 2x Intel RealSense D405 (stereo RGBD, 87°x58° FOV, 7-50cm depth); 2x LiDARs for navigation; joint encoders/IMU in DYNAMIXEL actuators.

Is it suitable for industrial deployment?

Yes, designed for manufacturing/logistics with IP unspecified but robust aluminum/plastic build, 0-40°C operation, 6kg dual-arm payload, and demos in real exhibitions showing 85% task success amid distractions.

How does teleoperation work?

Uses leader devices (FFW-LG2/LH5) with 22-60 DOF arms/hands/joysticks, streaming trajectories via ROS2 to follower. Supports VR/GUI for intuitive data collection in imitation learning pipeline.

What is the software stack?

ROS 2 Jazzy on Jetson AGX Orin, ros2_control (100Hz), Dynamixel SDK (RS-485), Docker for modularity. Supports Python/C++/Web UI; open-source with sims and tutorials.

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