How does artificial intelligence impact robot reliability?

Artificial intelligence (AI) has a significant impact on robot reliability. AI can help robots to learn from their environment and adapt to new situations, making them more reliable and efficient. For example, AI can help robots to detect and avoid obstacles, navigate complex environments, and perform tasks with greater accuracy and precision.

A robot executes its task with precision. However, the more precise you are the more deliberate you must be in commanded actions. As such, ML is best used for assistance with visual servoing or other recognition based tasks that determines how a robot interacts with a work piece, or in the case of mobile robots, determines how it reacts to it in a world space. AI and ML unfortunately do not learn on the fly well, so the idea that it can "improve robots to ... learn from feedback" is a stretch. It can however generate novel solutions, though novel is not necessary the most optimal.

With ever advancement comes challenges and setbacks. More often than not technology advances to fast not giving the engineers time to adjust for them.

I've worked with collaborative robots from KUKA and Fanuc since 2018. With progress in AI, robot perception (sensing - distance, shape, etc), modelling (making 3-D point clouds), of their local environment, and control (navigation and end-effector) movement continually improve. Non-linear improvements in both hardware & software are make robots more capable and reliable by the day.

As with any design, most will work under ideal conditions. How one works under ambiguous, unexpected or any other "non" ideal condition should be carefully thought out and tested before being used in any production environment

This is something that is not very well described. AI and ML do not change their behavior as they are introduced to new issues, they change their behavior as they are trained more. Testing the learning capability of ML or AI is something I've only seen successfully done in video games, as they can run through the same scenario extremely fast. I think training a robot using reinforcement learning would be difficult to do outside simulation due to time cost.

How can we test for the unknown? Unfortunately we can't. We can only make as many assumptions as we can and ensure the AI handles those in such a wa that it is intended to, and be sure to re-test often as things change

Testing is indeed crucial and expensive to sustain. Ai and ML are dependent on data which are ambiguous by the provider. Thus what constitutes testing based on these data's will definitely churn multiple results with different learning outcomes. What parameters of these testing are constantly questioned and leaves the Ai mapping it's processes to create unrelated and related outcomes. RL may be one of the best choices in obtaining desired results provided management of these processes, inputs, rewards, exploration, exploitation and variables are constant.

AI plays a significant role in improving robot reliability. Here are a few ways AI impacts robot reliability: 1. Fault detection and self-diagnosis: 2. Predictive maintenance: AI enables robots to anticipate maintenance needs by analyzing data from various sensors and historical performance. 3. Adaptive control: AI algorithms enable robots to adapt their behavior in real-time based on changing conditions.. 4. Learning from experience: AI-based machine learning algorithms allow robots to learn from past experiences and improve their performance over time. 5. Collaborative problem-solving: AI facilitates coordination and collaboration between robots operating in teams or alongside humans.

Many hypotheticals, unsure if they apply well to the topic at hand. I think the issue with operation is without robust status reporting you won't have good insight into the performance of whatever you are doing. The black box nature of any machine learning makes it difficult to ascertain issues, as opposed to traditional robotics and autonomy. Especially when it is very sure of itself.

AI as with any other tool is not a set it and forget it. It needs to be retested at regular intervals and maintained to ensure expected results

Many of these are confusing or fail to convey the issues at hand. The diagnostic sensors on a robot would already enable the use of a hypothetical self repair routine so the absence of these is not due to lack of intelligence but rather efficacy, and we can already allow for regular software updates. I think what it is trying to say is that updating/adding new features to any machine learning is hard, you don't really know if an updated model will do that much better without rigorous testing or if unintended bad behavior slips through.

Be sure to schedules checks at regular intervals and as larger changes in the environments are known to be upcoming. This can be built and automated with results sent to a human to review or if outliers are identified

Hypothetically, there was a machine that was supposed to do something based on how many pepperonis were on a pizza. ML was used to detect the pepperonis. This lead to far more errors than traditional detection techniques. The moral of the story is to try to use ML to improve problems traditional robotics and computer vision have not solved yet. As amazing as ML is, it's more amazing you can achieve 0.2 mm precision with an ABB robot due to some matrix math you can do by hand. And its more amazing you can run certain image filters and feature detectors in milliseconds on video frames using convolution math.

AI is a tool, not a solution. As things change, the solution may change and the tool needs to be checked and revised to account for those changes. Some changes can be proactively considered and built as part of the tool but over reliance on the tool to solve ALL problems can create havoc