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.
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.
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..”).
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
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!
[1] https://playground.mujoco.org/
MuJoCo is great. I have it running in the browser for robotics simulation. See for example https://visibot.com/sheet/examples/humanoid_walking.v
This is what StuffMadeHere used in his latest video to simulate a mini-golf course! https://www.youtube.com/watch?v=2OfjZ3ORJfc&t=368s
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.
That the calibration got the simulation so close to reality was quite impressive.
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.
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.
PS: I just finished a first draft for agentic skills around working with MuJoCo in Python. Feel free to check them out here: https://github.com/prathje/agentic_mujoco_skills
Mujoco is also key part of nvidias Newton physics system
https://github.com/newton-physics/newton
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..”).
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
People have made cool racing education simulators with this too: https://github.com/FT-Autonomous/ft_grandprix.