From Robot Grasping to Performance Anomaly Detection: Area of Normal Functioning and Exception Value

Many years ago, my PhD dissertation focused on industrial robot grasping processes and assembly accuracy using passive, sensorless adaptation. The practical problem was simple to describe but difficult to solve: how can a robot successfully grasp or assemble an object when there are inevitable errors in the object’s initial position, orientation, and geometry?

The main idea of that research was to calculate a set of initial conditions under which the grasping or assembly process would still succeed. I called this region the Area of Normal Functioning — ANF; in Russian, Область Нормального Функционирования — ОНФ.

In other words, ANF defined the “safe” or “normal” area of operation. If the initial coordinates of the object were inside this area, then passive mechanical adaptation could compensate for small errors and the operation would be successful. If the initial coordinates were outside this area, the process would likely fail.

Looking back, this idea has an interesting connection to my later work in IT performance anomaly detection. In my current research, I use the concept of Exception Value — EV — as the area between statistical limits and the actual observed values of system performance variables.

The domains are very different: one is industrial robotics, the other is IT system performance management. But the underlying idea is surprisingly similar.

In robotic grasping and assembly, the question was:

How far can the object’s actual position deviate from the ideal position while the robot operation still succeeds?

In performance anomaly detection, the question becomes:

How far can the actual value of a performance variable deviate from its statistically expected range before we should treat it as an exception?

In both cases, the main focus is not only the ideal or expected value. The more important question is the boundary between normal and abnormal functioning.

For industrial robots, ANF described the range of physical coordinates where passive adaptation was still able to correct errors. For performance data, EV describes the area where actual behavior moves beyond normal statistical expectations.

This connection is especially interesting because both ideas are based on “management by exception.” We do not need to react to every small deviation. We need to understand when a deviation becomes meaningful — when it leaves the normal functioning area.

Modern robotics research continues to explore related ideas under different terminology: passive compliance, compliant grasping, remote center compliance, sensorless robotic assembly, peg-in-hole insertion, and adaptive manipulation. Many recent methods also use sensors, machine learning, and vision systems. However, the older idea of defining a normal operating region remains relevant: successful automation depends not only on control algorithms, but also on understanding the tolerance zone where the process can still function correctly.

That is why I now see ANF as an early conceptual predecessor of my later EV work. ANF was about the boundary of successful physical operation. EV is about the boundary of normal statistical behavior.

Different fields. Different data. Same engineering mindset:

define the normal area, measure the deviation, and focus attention on meaningful exceptions.

 “conceptual comparison” table