tiny-differentiable-simulator
brax
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tiny-differentiable-simulator | brax | |
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6 | 11 | |
1,148 | 2,058 | |
0.9% | 3.3% | |
1.6 | 5.2 | |
12 months ago | 8 days ago | |
C++ | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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tiny-differentiable-simulator
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Brax vs TDS for differentiable rigid body dynamics
I need differentiable rigid body dynamics because I want to do nonlinear MPC. One library that can do this is C++ is Tiny Differentiable Simulator https://github.com/erwincoumans/tiny-differentiable-simulator. As I understand it, this software uses a C++ auto-diff library and code generation to create CUDA kernels to compute fast derivatives in parallel. This seems pretty fast because it's C++. Another option is Brax https://github.com/google/brax. Brax uses JAX which I've never used, but from what I've seen online, JAX is popular for researchers and probably very good.
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GitHub Actions by Example
https://github.com/google-research/tiny-differentiable-simul...
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Optick: C++ Profiler for Games
Yes, Chrome about://tracing is great to visualize your custom timing data. Happy used for the last 5 years in Bullet and recent physics engines, including events across tracing multiple threads:
https://github.com/google-research/tiny-differentiable-simul...
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Any tutorial on how to create RL C++ environments?
Or our C++ and CUDA Tiny Differentiable Simulator: https://github.com/google-research/tiny-differentiable-simulator
- I am new to Robotics. My first question is - Is MATLAB a important Programming language for Robotics?
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What Programming language/library to use for 3D visualisation of a robot arm?
Drake (and also tiny-differentiable-simulator that I know of) are using meshcat and it seems neat to me
brax
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4000x Speedup in Reinforcement Learning with Jax
There is Brax with its Ant, Humanoid and other rigid body articulated Gym environments: https://github.com/google/brax
- Physic engine for 3D simulation: which one to use?
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Brax vs TDS for differentiable rigid body dynamics
I need differentiable rigid body dynamics because I want to do nonlinear MPC. One library that can do this is C++ is Tiny Differentiable Simulator https://github.com/erwincoumans/tiny-differentiable-simulator. As I understand it, this software uses a C++ auto-diff library and code generation to create CUDA kernels to compute fast derivatives in parallel. This seems pretty fast because it's C++. Another option is Brax https://github.com/google/brax. Brax uses JAX which I've never used, but from what I've seen online, JAX is popular for researchers and probably very good.
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Deep learning for robotics
I am doing a MSc on robotics with a focus on machine learning, especially attention based architectures. There is a lot simulation and reinforcement learning going on. I have a funding of ~2500$ for the hardware system (no flexibility here, cannot use it for cloud either). I used pcpartpicker.com to select compatible components, as shown below. I am not located in the western part of the world; which means I have difficulty accessing some components and prices are higher here than that of pcpartpicker.com. That is why I am aiming towards 2000 - 2200$ range in the pcpartpicker.com. - Overall, what do you think of my planned setup? - Since there is a lot of simulation planned including rigid body dynamics with contact (libraries like https://github.com/raisimTech/raisimLib, https://github.com/deepmind/mujoco), I need some powerful CPU to use these libraries. I know that Intel has MKL over AMD; however, I am not sure how relevant that is for my case. The robotics simulators are generally written with C++, uses Eigen or their own math libraries. I feel like there is a lot of linear algebra involved and Intel combined with MKL should give me less headache. I have chosen i9-12900K, but what about AMD Ryzen9 5950X for example? - There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly. - Do you think this system is easy to upgrade in future? What can I change to make it easier for long-term use and upgrades? Thanks for any help!
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[D] Advice on Hardware Setup for Robotics
There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly.
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DeepMind open-sourcing MuJoCo simulator
I wonder what this means for the future of Brax [1].
1. https://github.com/google/brax
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Any tutorial on how to create RL C++ environments?
If you want raw speed, parallel execution on GPU or TPU is best. Checkout out our Brax simulator, which uses the XLA compiler and JAX Python frontend: https://github.com/google/brax
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Best environment to train RL agents
Check out Brax, hardware accelerated RL training in a Google Jupyter Colab. It trains typical RL tasks in minutes on TPU, also on GPU or CPU. And it is free, you can train with just a browser: https://github.com/google/brax
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[N] Mujoco is free for everyone until October 31 2021
Anyone made any progress with Brax? That was sold as a massively-parallel Mujoco alternative but not sure if anyone's actually using it yet.
- [R] Brax: A Differentiable Physics Engine for Large Scale Rigid Body Simulation, with a focus on performance and parallelism on accelerators, written in JAX.
What are some alternatives?
tiny-differentiable-simul
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
optick - C++ Profiler For Games
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
roadmap - GitHub public roadmap
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
RustyNEAT - Rust implementation of NEAT algorithm (HyperNEAT + ES-HyperNEAT + NoveltySearch + CTRNN + L-systems)
procgen - Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
ReinforcementLearning.jl - A reinforcement learning package for Julia
Numba - NumPy aware dynamic Python compiler using LLVM