I'd say RL is an advanced robotics topic. There are some books out there that cover introductory or general RL aspects... after that you'll have to dive into papers and specific code bases.
Might not be a bad idea to start with a Python-based, practical RL book and move from practical demonstrations towards theory.
Although not an RL book, I really like Data-Driven Science and Engineering by Brunton and Kutz:
If you're interested in perception, like tracking & freespace detection or SLAM, then "Probabilistic Robotics" by Thrun is pretty good. For a more fundamental take on that stuff (but less robotics specific), "Pattern Recognition and Machine Learning" by Bishop is my favorite.
Since you've mentioned exoskeletons, knowledge of kinematics and dynamics is imperative.
Rotation Matrices, Forward/Inverse Kinematics, Denavit - Hartenberg Parameters, Lagrangian Mechanics are a few fundamental concepts one should be familiar with. Their applications mostly pertain to robotic manipulators (arms), which are what members of exoskeleton's are modeled after.
They're covered extensively in the classic in the field textbook
Robotics Modelling, Planning and Control by Siciliano, Sciavicco, Villani, Oriolo
They also require some prior knowledge of linear algebra to safely navigate through, so make sure you've achieved at least some math literacy before diving into them.
Speaking of navigation, if you're interested in motion planning i.e. how to optimally (safely and efficiently) go from point A to point B, what you read is
for various ways the math people have came up with to solve this. Many cool applications in fields outside of robotics like in Computer Graphics/Animation too.
And btw, if there's one paper you'll absolutely have to read if you find yourself more interested in motion planning is
Sampling-based Algorithms for Optimal Motion Planning by Sertac Karaman and Emilio Frazzoli
in which the authors have revised two very popular path planning algorithms by making them significantly more optimal than their initial implementations were, and are part of many decision making systems that are involved in any type of mechanical movements.
Some other comments talked about more advanced disciplines in the field like State Estimation or Reinforcement Learning but I believe the aforementioned (kimenatics/dynamics/motion planning) are the bare minimum before diving into even more advanced math-heavy concepts.
Kevin Lynch has a great course "Modern Robotics: Mechanics, Planning, and Control Specialization" on coursera. It covers the key ideas in robotics at a high level (however it leaves out perception). I recommend it for people interested in getting started on this topic.
Similar questions get posted pretty often in engineering subreddits and it's difficult to answer as robotics is so multi-disciplinary.
Very few people in this world are skilled enough in embedded programming, PCB design (motor control and sensors), materials and structures, dynamics and kinematics, controls and machine design (CAD, selecting manufacturing processes, selecting various COTS mechanical parts like actuators and bearings) to be a one-man-robot making orchestra.
Advice to OP: pick a specialization to start with, and focus on learning that while using pre-made parts for the rest. For example, buy a ready-made robotic arm and write path planning and controls software to operate it. Or build your own CNC router/pick-and-place/3D printer/pen plotter but use off the shelf/open-source electronics and firmware.
Once that's done, pick another specialty and move on to that if it interests you.
Picking up this idea, someone should write a robotics version of "Build Your Own Metal Working Shop From Scrap"[0]. It's a seven volume series where basically you start with rocks, make a series of tools, and end up with a metal shop.
Even better if the parts for your robot(s) are machined using the DIY metal-shop tools! It's DIY all the way down...
All joking aside, I've always longed for a complete set of those Gingery books in print format. I have ebooks of most of them, but I'd love to have a bound set. And even more, I'd love to have time (and space) to actually take a stab at building the Gingery metal shop. Seems like it would be real blast (no pun intended).
There's plenty of toy arms for programming on every electronics site, including amazon. They're basically just a series of exposed hobby servos mounted together in a plastic skeleton. The motors are pretty much standard. You can even buy them with or without an control board.
Buy an Arduino Mega or something. That way you'll have way more pins that you'll need, and shouldn't hit up against the memory limits. Arduinos have a huge community, and the free IDE has all libraries and everything you need. I bought one and an MP3 shield from AdaFruit last year and successfully made a GNK droid. complete with flashing lights and multiple modes of tranquil gonks.
I bought a 6DOF robot arm a while ago, which uses servos driven by PWM, which I found quite fun.
It was only around £70 for the servos + metal framework. I drive it with a Pi and a PCA9685 based I2C board.
I was driving with a 5V supply, but it looks like the servos can run at 6V, which I need to try, as the bottom servo in the arm doesn't seem to have quite enough power.
I'd like to sometime try to 'teach' it to draw with a pen (however badly), 'Inverse kinematics' feels rather scary though, so wonder if anyone might have any very basic tutorials on this.
Tangentially related, I just bought 'The Ultimate Guide To DIY Animatronics' yesterday, which I'm looking forward to reading when it arrives.
And have been watching a few videos on animatronics such as:
Try the Robot Operating System documentation for the software and control side. If you can write the low level drivers then the ROS stack abstracts the IK and other middleware layers allowing you to focus on app and problem spaces.
The really big shift in ai in a sense was to reject the sense-model- plan- act cycle. I continue to see researchers assuming SMPA so although "everyone" knows this,"Intelligence without representation" by Brooks is essential. The alternative is sense-act in one layer, and model-plan over the top.
Well, from my experience in a partially automated factory 'wrangling the robots' I would do a little foray into 'things that can go wrong'. Our robot arms would periodically have 'robot revolt parties'. Watched a robot arm reach up and push a compressor right off a ten foot dead nest. It didn't drop it or fumble, it cleanly swept it right off. So many small things could trigger problems and sometimes the fixes were odd, like one robot refused to reset unless you opened and closed a certain one of the cage doors despite having done the proper reset process in the computer.
Nonlinear systems and control could be something that’s useful to get into regarding legged robots or exoskeletons. It’s not covered in depth by many general robotics textbooks but there’s a great lecture including fantastic interactive notebook material at http://underactuated.mit.edu/
I have heard good things about Foundations of Robotics Analysis and Control by Tsuneo Yoshikawa though haven't read it myself. Apparently it has a good coverage of the mathematics/equations needed for manipulation/control.
Somebody who has already read it might want to chime in.
The Robotics resources and texts by Peter Corke are good: https://petercorke.com/ -also has this good related course: https://robotacademy.net.au/
"Controls Engineering in the FIRST Robotics Competition" by Tyler Veness is free and a good short reference: https://controls-in-frc.link/
Also add the MIT Robotics Series books: https://mitpress.mit.edu/series/intelligent-robotics-and-aut...
Algorithms for Decision Making is free and awesome: https://algorithmsbook.com/
Also looks really good (MIT Press hardcopy): https://introduction-to-autonomous-robots.github.io/