AI and vision in robotics: recognizing an object is not enough to grasp it
From perception to grasp: Data, calibration and evidence to study vision-guided robotic manipulation.
Recognizing an object in an image and grabbing it with a robot are two different results. The first concerns the interpretation of visual data; the second also requires position, geometry, grip, movement and verification of the outcome. It is precisely this connection between perception and action that makes the meeting between artificial intelligence and robotics interesting.
EL-AI considers robotics as a possible development direction to explore. Here we explore questions to ask when evaluating a vision-guided manipulation. We do not present an existing EL-AI product nor an installation at a customer: the case of the component container is an example of study.
Describe the object before the algorithm
An opaque component, a reflective one and a transparent one pose different questions to those designing the acquisition. Before talking about AI model, collect variations, sizes, colors, packaging and conditions in which the objects arrive. Specifies whether they are sorted, overlapping, or partially hidden.
In our hypothetical scenario the robot must pick up a component from a tray and deposit it in a location. The goal is not to obtain a correct rectangle on the screen: it is to complete the deposit without damaging the piece and to recognize when conditions do not allow you to proceed according to the project.
Separate recognition and usable position
Vision can provide different information: presence, class, orientation or position in space. Ask which ones really serve the task and with what uncertainty they are available. A result sufficient to count objects in a photo may not be sufficient to insert a component into a seat.
The documentation for Universal Robots' AI Accelerator describes a set of tools for integrating perception, computing, and robotic applications. It is an example of the existence of platforms for developers; it does not demonstrate that each object or process is already manageable without integration activities.
Understanding the role of calibration
To use visual observation in motion, the system must link the camera references to those of the robot. The camera calibration manual explains this connection and points out the need to repeat it when conditions affecting the camera position change. Specific procedures depend on the hardware and must follow its documentation.
For the project plan, the practical question is how to recognize that a configuration is no longer valid. A maintenance activity or a change of equipment may require checks agreed with the integrator. Keeping the configuration versions and the reason for the changes helps to reconstruct the test results.
Build a representative set of tests
Collect images and conditions that represent the intended work, including difficult but plausible cases. Avoid using only selected photographs because the system interprets them well. Distinguish the materials used for development from those reserved for verification, so as to observe the ability to deal with cases not already adapted during preparation.
- Variants in shape, orientation and arrangement permitted by the project.
- Realistic lighting differences for the intended environment.
- Objects partially covered or close to the edge of the container.
- Empty container, unknown object and unusable image.
- A failed grip and a placement that requires a subsequent check.
Each case must have an expected outcome and a method of managing uncertainty defined by those who design the application. The absence of a reliable response should not be hidden in an overall average: it should be recorded as a condition to be understood.
Measure the entire task
Store the outcome of the perception, gripping attempt, placement and required interventions separately. If the part is correctly identified but the grip fails, modifying the model may not resolve the cause. The distinction allows us to discuss with different skills without reducing every problem to the word "AI".
A credible test states which variants it includes and which it excludes. For EL-AI, delving into this area means studying how software, data and physical systems can work together. The useful first step is a narrow question with observable criteria, not a promise of general autonomy.
Content prepared with AI assistance; sources consulted September 19, 2026. Illustrative scenario, not customer outcome. Cover generated with AI.
Illustrative AI-generated cover; it does not depict actual EL-AI people, premises or installations.