A robot arm's specifications determine the movements it can perform, the size of the workspace it can operate in, and whether it can integrate with existing research systems. When choosing a robot arm, comparing only degrees of freedom, payload, or price can easily lead you to overlook factors that have a real impact on project progress.
The following specifications deserve particular attention.
Degrees of Freedom (DoF)
Degrees of freedom (DoF) are an important indicator of a robot arm's motion capabilities. A higher DoF count generally allows for a wider range of pose adjustments, but it also increases the complexity of motion planning and control.
4–5 DoF: Suitable for some basic grasping, pick-and-place, and positioning tasks.
6 DoF: Provides a broad range of position and orientation control capabilities and is a common configuration for many general-purpose manipulation robot arms. For example, the LEMO A1Z can easily handle most embodied AI research, experimentation, and development tasks.
7 DoF and above: Provides additional kinematic redundancy, which can help the robot avoid obstacles, adjust its elbow configuration, or optimize its motion path.
These are general guidelines rather than absolute performance thresholds. Actual capabilities also depend on joint configuration, range of motion, control algorithms, and end-effectors.
For Embodied AI development, the choice of DoF should be guided by the poses and movements required for the target tasks, rather than by simply pursuing a higher DoF count.
Payload Capacity
Payload refers to the weight a robot arm can carry. When selecting a robot arm, it is important to distinguish between rated payload, peak payload, and the actual payload permitted under different motion conditions.
In particular, the effective payload must account not only for the object being handled, but also for the weight of the gripper, cameras, mounting brackets, and other end-of-arm equipment. The LEMO Series robotic arms support a peak payload of up to 5 kg per arm, giving researchers and developers the flexibility to integrate a wider range of end-effectors for embodied AI research, robotic manipulation, and custom development—without being limited by hardware constraints.
If grasping or material handling needs to be performed at high speeds, you should also consider how changes in payload affect motion accuracy, vibration, and stability.
Workspace and Reach
The workspace determines the positions a robot arm can reach. Reach is commonly used to describe the distance the end effector can cover, although definitions and measurement conditions may vary across manufacturers.
When selecting a robot arm, check the following:
Whether the robot arm can cover the target work area.
Whether there are edge positions or orientations that are difficult to reach.
Whether the mounting configuration affects the actual workspace.
Whether a dual-arm system has overlapping workspaces or potential collision risks.
For tasks such as tabletop manipulation and precision operations in laboratory environments, an appropriate workspace is often more important than simply choosing a robot arm with a longer reach.
Repeatability and Motion Performance
Repeatability measures a robotic arm’s ability to return to nearly the same position when repeating the same motion command. It is different from absolute positioning accuracy, which measures how closely the arm reaches its intended target.
The Galaxea LEMO Series robotic arms deliver repeatability of up to ±0.1 mm, providing the precision and performance needed for a wide range of embodied AI research, robotic manipulation, and development tasks.
End-Effectors and Sensor Interfaces
A robot arm cannot perform every type of manipulation on its own. Grippers, suction cups, dexterous hands, cameras, and force sensors all affect the range of tasks the system can perform.
For example:
Parallel grippers are ideal for picking up a wide range of regularly shaped objects. The Galaxea G1Z gripper delivers up to 150 N of gripping force for reliable grasping performance.
Dexterous hands are useful for research into multi-finger manipulation and complex hand movements.
Wrist-mounted cameras provide visual information for close-range manipulation.
Force/torque sensors help researchers study contact conditions and precision manipulation.
Before purchasing, verify that the end-effector interface, mounting holes, communication methods, power requirements, and supporting software meet your needs.
Control Interfaces, SDKs, and APIs
For researchers and developers, the openness of the software ecosystem may be more important than any single hardware specification.
Key points to check include:
Whether an SDK, API, and clear development documentation are available.
Whether common programming languages and development environments are supported.
Whether joint states can be read and control commands can be sent.
Whether the required control modes, such as position, velocity, or torque control, are supported.
Whether ROS interfaces or other standardized integration options are available.
Whether custom control programs, data recording, and system extensions are supported.
If a robot arm can only be operated through a closed graphical interface and does not provide access to the necessary state data, it may be suitable for basic demonstrations but not necessarily for in-depth Embodied AI research.
Safety and System Reliability
Robot arms can generate substantial motion inertia and contact forces during operation. Safety design is especially important for dual-arm systems, high-payload systems, and experimental environments where people work in close proximity to the robot.
Key safety and reliability considerations include emergency-stop mechanisms, joint soft and hard limits, collision detection, fault recovery, communication-loss handling, and equipment installation and operating requirements.