If you’ve ever tried teaching a robot to move or navigate, you know how slow and tedious hardware testing can be. You risk damaging expensive equipment, and physical trials take a long time to reset. That’s where physics engines and simulators come in, such as the open-source physics engine Brax from Google Research that we will discuss in this post.
With Brax you can simulate not just one but many robots in parallel using GPUs or TPUs (Graphics/Tensor Processing Units, which are specialized hardware great for fast, parallel math). And yes, it can also run on a CPU, although it will be at a slower rate. This dramatically accelerates reinforcement-learning (RL) experiments—the process of training a robot through trial-and-error using rewards and penalties—compared to traditional CPU-bound simulation tools.
Brax is built with JAX (a powerful framework for high-performance machine learning) and is designed for robotics and reinforcement learning tasks where control policies are learned through trial and error. Rather than simulate one robot at a time on a standard CPU, Brax allows you to run hundreds or more environments simultaneously on accelerators. It is distributed under the Apache 2.0 license, is free to use, and is JAX-native with potential ML framework extensions (e.g., TensorFlow or PyTorch via notebooks).
Imagine you’re working on a small two-wheeled robot that must drive, turn, and avoid obstacles on its own. In Brax you define the robot’s structure—its wheels, body, joints, and masses—and the engine takes care of rigid-body dynamics, collisions, and other physics. You then define how you control the robot (for example, torque commands for each wheel) and what you want it to achieve—a reward for reaching a goal and perhaps a penalty for colliding or wasting energy.
Real-World Example: Training a Warehouse Robot with Brax
(This is an illustrative scenario; Brax includes real environments like UR5e for industrial robot arms or Grasp for dexterous manipulation tasks.)
Consider a logistics company that needs to train a four-legged warehouse robot to autonomously pick up and sort packages.
Define Rewards (The Goal): The robot is given a high reward for dropping the correct package on the conveyor belt and a penalty for bumping into obstacles (like pallets) or running out of energy.
Massive Parallel Training (The Speed): Instead of running one robot in a real, slow warehouse environment, Brax runs thousands of copies of the robot simultaneously in simulation. Each copy tries different ways to walk and navigate.
Result: Because thousands of robots are learning in parallel, an optimal control policy can be found in hours instead of the weeks it would take with traditional methods.
You proceed to train the robot with reinforcement learning. Because Brax supports many parallel simulations, you can let many instances learn simultaneously and converge faster. Once you have a trained controller (such as a neural-network policy), you can deploy it to your real robot—using an onboard computer like a Raspberry Pi or Jetson Nano (though Arduino works for simpler tasks, it may fall short on processing power for complex RL policies). However, moving from simulation to reality (Sim2Real) still demands careful calibration: sensors, actuators, friction, latency—all differ in the physical world.
In essence, Brax is optimized for simulating many robots quickly—prioritizing speed and scale with parallel processing and differentiable physics—rather than modeling every nuance of the real world. It edges out tools like MuJoCo (strong on fidelity but slower for batches) or PyBullet (great for beginners but less scalable), making it ideal for training control strategies, robotics research, algorithm development, and machine learning exploration. If you need photorealistic rendering or complex sensors, complement it with other tools. It’s free, open-source, and maintained by Google Research. In short, it lets you experiment with “robots in code” at scale—while bearing in mind that getting them to work in real life still takes work.
Installation is simple if you have Python set up: pip install brax uses pip (Python’s built-in package manager) to quickly download and install the Brax library from its official repository. You can then launch one of the included benchmark environments—pre-built, standardized RL scenarios for testing algorithms like walking or running—such as “ant” (a multi-legged robot learning balance and navigation) or “halfcheetah” (a streamlined runner optimizing speed and gait), and start simulating or training a basic robot policy in minutes. For anyone exploring robotics, reinforcement learning, or control-policy design, Brax offers a powerful and accessible entry point.
Post By: A. Tuter
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