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He did

That’s a difficult problem with some work arounds. You could program some kinematic vector equations (ex. What angle does the laser need to point at from an origin in order for the vector to touch a specific point on another chord system), but this is difficult because angled surfaces in the room would each require their own 3D equations. Instead, use PID control equations. Ex, I want the laser to be “here” but the laser is “there” it is off from its target by 50 px in the y and -40 px in the left. The Pid control equation would basically keep working until both of those number are zero. This is the easier solutions but takes more tuning (changing the constants in your transfer function), but the experimentation would be fun!

@@PK-wo4vo Have you considered a "calibration" phase, to identify the unknown of the coordinate mapping? That would be an alternative to the PID that requires the laser dot to be visibile at all times.

ChatGPT is mainly used for mistakes in code though, because it it codes something you do not understand, it does not count as “your work”, and in the future it might not be there, your choice though.

​@@NaniteKnightuse it to write the bulk of it, then read every line and fix the errors. AI is the new pocket calculator.

its to hard lol

then it doesn't need motion sensor, but face detection and recognition

Only needs tensorflow for color and posture recognition, why would you need facial recognition?! I get it but it’s so much work. Just do color and posture recognition to check objects color then posture and shoot?

@@Darkshadow-ll8geI don’t think you understand how expensive and difficult that actually is

Thanks for the feedback! I went ahead and tried to improve the thumbnail, hopefully it's better.

Thanks, I really appreciate it! I'd like to go more in depth into all those topics. I'm new to making videos so I'm still learning how to script these things and then put them together. All the videos on this channel with me not speaking in them are videos that, at the time, I simply wasn't good enough at writing to say anything in them. To answer your questions, "ignore aerodynamics" means that for each wing along with the x, y, and z rotation analogue outputs, they also have a digital output that just turns the aerodynamics force on or off for that wing. This means the bird can turn the aerodynamics off when lifting the wings, then turn them on when pushing them down, which allows them to fly. Birds in the real world can just angle their wings to get this lift imbalance, but I don't know enough about bird wings to model them as I have to make all the joints myself, so I went with a simpler solution. As for the step, to be honest, I've not been good at keeping my old data, so I can't tell you exactly how many steps I did, but with my recent simulations of the birds, in 90 minutes my computer can do about 3 million steps so I would guess probably 3 million, maybe 4 million. This is reinforcement learning and is all part of Unity's ML Agents library that does the machine-learny stuff, so I need to make the environment, the agent, the inputs, the outputs, and the reward, then ML Agents uses PPO to train the agents. The doc can be found here: github.com/Unity-Technologies/ml-agents/blob/release_18_docs/docs/Readme.md I uploaded a new video yesterday that goes into getting the birds to fly round an obstacle course: th-cam.com/video/uUGIik-K05s/w-d-xo.html

That's a good question. I was planning on making this video into a full video explaining it all, but I don't yet have the script writing skills to make it good. The way to find a good strength is: first get a good starting value (normally 0.01), then do a little trail and error I trained with 0.01 curiosity (which is in the middle of the typical range 0.001-0.1 from the docs) and got ~4 extrinsic reward (~40 metres / 10) and ~1 intrinsic reward, so ~20% of the total reward is from the curiosity. Normally 20% is pretty high, but I'm using curiosity to change how to stickman walks, instead of using it to explore an area passively, so it needs the high % for this to work. I tried a few values (in the order I tested: 0.01, 0.1, 0.05, 0.02, 0.03, kind of like a binary search) At strength 0.01, the curiosity was too small to affect training. Strength 0.02 sometimes did, sometimes didn't affect training. Strength 0.03 and 0.05 always led to walking instead of galloping. When I tried strength 0.1, it did learn to walk, but it learned so slowly that it ran out of training time before getting to the second obstacle. Basically, any strength 0.03 or above always gave walking, but the higher I went over 0.03, the slower it trained (about 80% training speed at strength 0.05, and ~40% training speed at 0.1). So the reason I use 0.03 in the video is because it worked, and it trained the fastest out of the 3 that did, giving it more time to perfect its walking. docs link: github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Training-Configuration-File.md#curiosity-intrinsic-reward also, I got the intrinsic strength information using Tensorboard. It's pretty useful, I would recommend it.

no u

Thanks! I've made the github repo public, so hopefully you will be able to find it all here: github.com/ThomasLynn/Stickman-Walker Good luck on your project!

@@zoelynn126 Thanks!

Well, me too =/ still can't figure out how to script this one. Thought I'd upload something though, as it was getting to a point of me just staring at a crappy script for days on end.

Fair enough