Not sure why this is hitting the home page right now but people may also be interested in Mujoco Playground [1] which is the latest RL environment wrapper of mujoco, implementing both classic deepmind-control benchmarks, and some very new interesting ones!
The physics engine I'm using is called MuJoCo. And if you're wondering why I didn't write my own physics engine, it's basically because I don't have 20 years.
It's what put MuJoCo on my radar recently! But I was surprised to not see him do any kind of gradient descent to optimize his hyperparameters. MuJoCo has a JAX backend so it should be fairly straightforward.
I'm pretty sure he has used gradient descent in previous videos to optimize systems, maybe this time it was just easier to hand tune rather than set up an optimization feedback harness around MuJoCo.
Though he had to resort to manual calibration. I always find he has interesting problems for domain experts and would like to see him team up with one. Also with programmers for faster programs than self taught python.
In the video in question, he doesn't seem able to choose a good scoring function for the stochastic solver (even over multiple weeks), seemingly choosing a linear sum of distances (see 8:50) between simulation and reality. That's a mistake that not even an undergraduate should make. He needs some domain experts.
While we have people's attention here, we're also working on an official browser-based interactive viewer that allows people to specify a URL to their own model and share a link.
We are using MuJoCo to train a G1 humanoid robot right now. The best thing is that we do not need to fight with NVIDIA software and that it runs on macOS.
This makes me so happy and excited! Often my mind wanders into the unknown, imagining what would happy to X if it did this? Would it have friction, etc?
I am looking forward to a way I can easily describe a scenario and have an LLM build a legitimate simulation for it. No more hypothetical talk! Next best thing to actual experimentation (can be a useful tool in convincing others to join you/support you in said real experiment - “see? I tested it in a simulation and it behaves exactly that way! We need to try this..”).
One thing to be mindful of is that you can get a simulation to behave in (almost) any way you want if you set the parameters right, so you should take care to understand the assumptions that you're baking into your sim before taking its results as gospel.
Build things yourself. Using LLMs doesnt help you understand anything, they will just give you an annoying case of dunning kruger. Using them will only make you retart-d
[1] https://playground.mujoco.org/