physics simulator
In plain wordsSoftware that imitates how real objects move, collide, bend and fall, so robots and their AI can be designed, trained and tested on a computer before touching the real world.
Software that numerically integrates the equations of motion of a virtual world — rigid bodies linked by joints, contacts and friction, sometimes soft bodies, cloth and fluids, plus simulated sensors such as cameras and lidarlidar, n.: A sensor that measures distances by firing laser pulses and timing their reflections, building a 3D picture of its surroundings out of millions of points; widely used on robots and self-driving cars. — so that robots can be designed, programmed, trained and tested without hardware. At each small time step a simulator detects collisions, solves for contact and constraint forces (the hard and approximate part) and advances positions and velocities. Robotics simulators couple a physics engine with robot models (joint limits, motors, sensors) and rendering; widely used ones include MuJoCoMuJoCo, prop. n.: A free physics simulator, now maintained by Google DeepMind, that AI researchers use to train and test simulated robots and bodies, especially with trial-and-error learning., Bullet/PyBullet, Gazebo, Drake, NVIDIA's Isaac Sim and Isaac Lab, and Genesis. GPU-parallel simulators run thousands of environments at once, which made large-scale reinforcement learningreinforcement learning, n.: A way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time. for robots practical, and differentiable simulators expose gradients for optimization. Trade-offs between speed, accuracy and stability, especially for contact, create the reality gap that sim-to-real transfersim-to-real transfer, n.: Training a robot's AI in a computer simulation, where practice is fast, cheap and safe, and then making it work on the real robot despite the differences between simulation and reality. must bridge. Learned video world modelsworld model, n.: An AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real. are an emerging data-driven complement.
We run 4,096 simulated quadrupeds in parallel on one GPU and get a walking policy in an hour.
Origin
Robot simulation dates to the 1980s. Open-source engines such as the Open Dynamics Engine (Russell Smith, 2001) and Bullet (Erwin Coumans, mid-2000s) and the Gazebo simulator (Nathan Koenig and Andrew Howard, 2004) made it routine, followed by MuJoCo (2012) and GPU-parallel simulators such as NVIDIA's Isaac Gym (2021).
Usage note
Game physics engines favor speed and plausibility; robotics needs accuracy, stability and repeatability, especially for contact, so the two families have diverged.
Related
- MuJoCoMuJoCo, prop. n.: A free physics simulator, now maintained by Google DeepMind, that AI researchers use to train and test simulated robots and bodies, especially with trial-and-error learning.
- sim-to-real transfersim-to-real transfer, n.: Training a robot's AI in a computer simulation, where practice is fast, cheap and safe, and then making it work on the real robot despite the differences between simulation and reality.
- domain randomizationdomain randomization, n.: Training a robot's AI in a simulation whose colors, lighting, weights, friction and other details are shuffled at random on every run, so that the real world holds few surprises it has not already practiced for.
- digital twindigital twin, n.: A detailed virtual copy of a real machine, factory, robot or city, kept in sync with live sensor data and used to monitor it, test changes safely and predict problems.
- world foundation modelworld foundation model, n.: A large AI model trained on vast amounts of video that can imagine how a scene will unfold, used to create realistic practice worlds for robots and self-driving cars.
- GymnasiumGymnasium, prop. n.: A free software library that gives reinforcement learning programs one standard way to plug into practice tasks, from simple games to simulated robots, so methods can be tried on identical problems. It succeeded OpenAI's Gym.
- reinforcement learningreinforcement learning, n.: A way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time.
- world modelworld model, n.: An AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real.
Sources
- Koenig, N. & Howard, A. (2004). Design and use paradigms for Gazebo, an open-source multi-robot simulator. Proc. IEEE/RSJ IROS 2004.
- Todorov, E., Erez, T. & Tassa, Y. (2012). MuJoCo: A physics engine for model-based control. Proc. IEEE/RSJ IROS 2012.
Mentioned in
physical AIphysical AI, n.: AI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them., world modelworld model, n.: An AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real., digital twindigital twin, n.: A detailed virtual copy of a real machine, factory, robot or city, kept in sync with live sensor data and used to monitor it, test changes safely and predict problems., environmentenvironment, n.: Everything an AI agent interacts with but does not control — a game, a simulator, a website, the physical world. It answers each of the agent's actions with a new situation and, in reinforcement learning, a score., locomotionlocomotion, n.: How a robot moves itself from place to place: rolling on wheels, walking on legs, crawling, swimming or flying., robot learningrobot learning, n.: Teaching robots skills from data and experience, such as watching demonstrations, practicing by trial and error or training in simulation, instead of programming every movement by hand., Robot Operating SystemRobot Operating System, prop. n.: Free, widely used software that serves as the plumbing of many robots, letting separate programs for sensors, planning and motors pass messages to each other, with a large library of ready-made tools., sim-to-real transfersim-to-real transfer, n.: Training a robot's AI in a computer simulation, where practice is fast, cheap and safe, and then making it work on the real robot despite the differences between simulation and reality., domain randomizationdomain randomization, n.: Training a robot's AI in a simulation whose colors, lighting, weights, friction and other details are shuffled at random on every run, so that the real world holds few surprises it has not already practiced for., MuJoCoMuJoCo, prop. n.: A free physics simulator, now maintained by Google DeepMind, that AI researchers use to train and test simulated robots and bodies, especially with trial-and-error learning., world foundation modelworld foundation model, n.: A large AI model trained on vast amounts of video that can imagine how a scene will unfold, used to create realistic practice worlds for robots and self-driving cars.