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AgileX NERO 7-DoF Robot Arm
AgileX NERO 7-DoF Robot Arm
AgileX NERO 7-DoF Robot Arm
AgileX·NERO·ARM-015

AgileX NERO 7-DoF Robot Arm

$3,498.90
Financing available Pay later on net terms, or monthly over 12 to 60 months.See options →

The AgileX NERO is a seven-axis research arm with built-in collision protection, controlled over CAN, HTTP or TCP.

Shipping
Free on every order
Duties and tariffs
In the price, nothing extra at checkout
Minimum order
1 unit
Datasheet (PDF)Need volume pricing or a custom configuration? Tell us what you need →
  • Free shipping On every order.
  • No tariffs at checkout Duties are in the price. Nothing to pay on delivery.
  • US seller of record US invoices, terms and support.
  • Maker's warranty Typically 12 months. Unused items can be returned within 30 days.
Research
Class
3 kg
Payload
580 mm
Reach
7
Axes (degrees of freedom)
±0.1 mm
Repeatability
Estimated from motor current
Joint torque sensing

Specifications

Performance

Class
Research
Payload
3 kg
Reach
580 mm
Axes (degrees of freedom)
7
Repeatability
±0.1 mm

Mechanical

Weight
4.8 kg

Electrical

Supply voltage
24 V

Integration

Controller
Built in; CAN, HTTP or TCP
Cartesian control (built-in IK)
Yes
SDK and interfaces
Python SDK (pyAgxArm), ROS 1, ROS 2; CAN/HTTP/TCP; drag teaching, offline trajectory
Simulation
URDF / RViz 2 (ROS 2) and Isaac Sim URDF (open_nero repo)
Joint torque sensing
Estimated from motor current
Grippers
AgileX AgxGripper (supported in pyAgxArm)

Safety

Collision detection
Yes

Specifications and photos are the maker’s published data. Download the datasheet (PDF).

Made by

AgileX
2 products

Typical applications

  • Machine tending and lab automation
  • Pick and place, kitting and test stations
  • Robot learning and teleoperation data collection
  • Education and research labs
  • Mobile manipulators

Choosing the right one

  • Size payload for the gripper, camera and part together, with margin: a 5 kg arm carrying a 1 kg gripper has 4 kg left.
  • Rated payload holds at full reach. Most arms carry less when fully extended at full speed.
  • Check that the controller takes Cartesian targets and solves the inverse kinematics itself; otherwise you write that math.
  • Torque sensors in every joint make force control, collision detection and hand-guiding far better than current-based estimates.
  • Try the vendor's simulator or URDF before you buy. A clean Python API and simulator are a good sign of the support you will get later.
  • Hobby servo arms (under about $1,000) suit teaching and prototyping, not production duty cycles.

Related guides

About this product

Mounts on a table, wall or ceiling; built for embodied-AI and humanoid research.

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