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+---
+title: Robotics at a High Level
+date: 2023-07-26
+categories: [Robotics]
+tags: [study, robotics, engineering] # TAG names should always be lowercase
+---
+
+
+* TOC
+{:toc}
+
+## What are robots?
+
+It's a pretty broad term when you think about it. [Wikipedia](https://en.wikipedia.org/wiki/Robot)
+says that "A robot is a machine --- especially one programmable by a computer --- capable of
+carrying out a complex series of actions automatically". Here _automatically_ is meaning that at
+least part of the control is the responsibility of the machine, usually via algorithms implemented
+by the engineers who designed it. In other words, it make take some instructions from people via a
+controller or other means however at least some of the decisions are made by the robot. For example,
+quadcopter drones could be considered to be a kind of robot since even though humans usually use a
+wireless controller to give them instructions, the drone uses control algorithms designed by an
+engineer to ensure its stability _automatically_.
+
+ _A quadrocopter drone with a wireless controller_
+
+Of course, there are many examples of robots that perform there work without much control from
+humans. The robotic arms that are now ubiquitous in manufacturing industries spring to mind. They
+just go about there job, doing what they have been programmed to do with very minimal human
+interaction required.
+
+ _Some robotic arms used for manufacturing in a factory_
+
+And of course, no overview of robotics would be complete without mentioning
+[Boston Dynamics](https://bostondynamics.com/) and their backflipping robots.
+
+
+
+## What are the common problems robots must solve?
+
+Well this can be quite dependent on the application but to perform high level tasks like
+self-driving cars or even something more mundane like a personal assistant bot that can make me a
+sandwhich there are three big problems to solve.
+
+Firstly, _where am I?_ Solving this requires what is known as localisation. In other words, the
+robot must have some representation of its environment and its location in it. OK, so now that the
+robot knows where it is now it needs to know _where do I need to go?_ Of course we know that to make
+a sandwhich you should probably find your way to the kitchen and then find the fridge and get out
+the food you need e.t.c but this is all implicit when I ask my robot to _make me a sandwhich_. We
+know this because of experience and implicit meaning which in general is fairly difficult to
+directly program into a robot. Ontop of that, the robot now needs a concept of a kitchen and where
+in its map or representation of the environment the kitchen could be. Again, very difficult to come
+up with exact instructions for --- we just know because of our experiences. Finally, even once our
+robot knows this, it must plan its path through the environment in order to reach the kitchen. It is
+almost certain that on its way, the robot will encounter static obstacles like tables or walls and
+maybe even dynamic obstacles like people or pets. When I ask my robot to make me a sandwhich I
+didn't directly say it but it would be nice if it didn't blast a hole in my wall to get there --- or
+worse my cat. So it needs to do some path planning and likely make dynamic changes to it as it makes
+its way.
+
+## See, Think, Act
+
+As just discussed, most of the time for robots performing high level tasks they must constantly be
+updating their plan as the environment around them changes. This is often known as the _see, think,
+act_ cycle.
+
+First the robot gathers raw data about its environment and itself using sensors such as cameras,
+ultransonic sensors, lidars, microphones, IMUs e.t.c. Already we have problems since the data
+collected by the robot will be invariably _wrong_. Sensors are inherently vulnerable to noise in the
+environment (information we don't care about) but also in most cases robots are _discrete_ systems
+that live in a _continuous_ world. They are limited in the information that they can perceive in
+terms of how often (temporal discretisation) and also how accurate (quantisation) it is. For the
+noise problem, in many cases the frequency of the noise is much higher than the desired recordings
+and so a low pass digital filter can be used to help attenuate it but there is little that can be
+done about temporal discetisation and quantisation other than the limitation of your hardware and
+choose it appropriately for the task. Interpolation and other tricks are possible but in any case
+the amount of information is fundamentally limited by the sampling rate and bit precision.
+
+Now, after obtaining this information about the world, the robot must use it to make decisions.
+Fundamentally, the data it has gathered is generally represented in very simple ways. Pixels are
+just values in a grid (or array) representing the amount of red, green and blue in a small area of
+the world. In order to gain some kind of useful understanding, the robot must use some kind of
+algorithms or models to interpret the relationships and patterns in these grids of numbers. Somehow
+we need to get from these grids of numbers to an understanding of where the kitchen is. This is
+certainly not a trivial problem and something that has been a significant struggle. In the last
+decade there has been impressive progress with the advances in deep learning. Instead of writing
+algorithms for computers to execute, we found if we wrote algorithms to help the robots learn the
+best algorithms to use by presenting it with lots of data this worked quite well. Pretty meta.
+Convolutional Neural Networks were able to learn these layers of abstraction naturally using learned
+kernels \(see my blog post [here]({% post_url 2023-05-23-an-intro-to-transfer-learning %}) for some
+more details on CNNs\) and more recently, Vision Transformers (ViTs) have been able to achieve
+state-of-the-art performance on imaging tasks. At the same time, computational power has continued
+to grow with particular focus on parallelisation (performing multiple computations concurrently)
+which is particularly significant for imaging tasks and training these neural netowrks. Furthermore,
+once the robot has used these models to gain some model of its environment, other AI techniques such
+as reinforcement learning have proved to be effective in helping it to make decisions based on the
+information about its environment.
+
+This leads to the final stage in the cycle which is to _act_ in some way. In my case, hopefully the
+robot decides to start moving towards the kitchen and not into my cat and to do so it will probably
+turn some motors to move its wheels or legs or something like that. This in turn will affect the
+robots state and potentially the environment around it so the whole process can repeat as it begins
+gathering information about its environment again.
+
+So a pretty high level overview in the first week of my METR4202 course. I'm pretty excited to learn
+more about the details of these parts in the upcoming weeks.
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