Probability theory and statistics for Robotics and ME

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Discussion Overview

The discussion revolves around the importance of probability theory and statistics in the fields of control theory and robotics. Participants explore how these concepts apply to real-world scenarios, particularly in the context of sensor data and system optimization.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Homework-related

Main Points Raised

  • One participant expresses a belief that their understanding of probability and statistics is deeper than that of their colleagues and questions the value of this skill in their field.
  • Another participant emphasizes the significance of probability and statistics in control theory, noting that feedback and random behavior are inherent in all human efforts.
  • The use of Kalman filters is highlighted as a method for combining sensor information while accounting for reliability and random influences.
  • An example is provided regarding airplane flight control, illustrating how complementary filters can be used to merge high-frequency and low-frequency data for better accuracy.
  • Participants seek recommendations for books and professional directions related to probability and statistics in control theory and robotics.
  • One participant mentions that Kalman filters are covered in many optimal control textbooks, suggesting that foundational knowledge in control laws, multivariate statistics, and optimization is necessary.
  • A link to an advanced resource on stochastic optimal control is shared, although it is noted that it may be intimidating for those without sufficient background knowledge.

Areas of Agreement / Disagreement

Participants generally agree on the importance of probability and statistics in control theory, but there is no consensus on specific book recommendations or professional directions tailored to the original poster's background and interests.

Contextual Notes

Limitations include the original poster's uncertainty about their background and the varying levels of familiarity with advanced concepts among participants. The discussion does not resolve which specific resources or career paths are most suitable.

debelino
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I study control theory and robotics. Recently I figured out that I have a much deeper understanding of probability and statistics compared to my colleagues. Is this 'talent' valuable in my field and if so, where? We used this theory to define white noise, but nothing more...as of now.

Also I am generally good in math and physics and I have a high IQ. I am lazy to do repetitive work and because of all this I think a good strategy for me would be to specialise in a field full of hardcore theory to reduce competition by taking advantage of my scientifically oriented mind. It is easy for me to make connections between theory and practice and fulfils me to see that something I theorized about is working in the real world.

I need advices for my career. What should I concentrate on? Any book recommendation is also welcome. Sorry for my English, it is not my first language.
 
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Probability and statistics is very important in control theory. In every human effort, there is feedback, random behavior, and optimization. Regarding random behavior in control theory, your input sensors are not perfect and have a random component. Kalman filters are used to combine information, taking into account the reliability of each of the inputs. Also, there are usually random influences that can not be measured at all.
For a simple example that doesn't require Kalman filters, suppose an airplane flight control needs to estimate lateral velocities. It can integrate accelerometer inputs to get very fast reactions that, unfortunately, will drift and accumulate an error. It can also get GPS information at a much lower rate which does not react fast but does not drift. A complementary filter can be used to combine the high frequency input from the accelerometer with the low frequency input from the GPS and get the best of both in a single lateral velocity signal.
 
Thank you for your fast replay.
Can you recommend me good books on this topic? What professional direction is good in my case?
 
debelino said:
Thank you for your fast replay.
Can you recommend me good books on this topic? What professional direction is good in my case?
Unfortunately I am not up to date on current textbooks. I also do not know what your background is. Kalman filters are in many books on optimal control, but it is probably after a lot of preceding material. To get the background material, you can look at your university texts on control laws, multivariate statistics, and optimization.

Just to get an idea of what is involved, you may be interested in https://www.utdallas.edu/~sethi/Prosper/Control-Tex-Chapte13.pdf . It is advanced and I don't know what your background is, so don't worry if it looks intimidating. I am just mentioning it to show you how central statistics is in a field like stochastic optimal control.
 

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