Key takeaways
- Figure 1: Humanoid robot locomotion simulation.
- Collecting this kind of data in the real world is slow, expensive, risky, and sometimes impractical due to the destructive nature of the tasks.
- Earlier robotics simulators were often used primarily to debug geometry, test controllers, or visualize robot motion.
What happened
Figure 1: Humanoid robot locomotion simulation. The robot's pose is represented by tracked body keypoints (green markers), while successive robot instances illustrate its movement through time. Directional arrows indicate commanded motion, demonstrating the use of a physics-based simulation environment for training and evaluating robot locomotion and control policies. The primary challenge in building physical AI systems is data availability.
Many simulation engines are available today, each targeting different use cases such as humanoid robots, aerial vehicles, ground robots, autonomous vehicles, quadrupeds, and dexterous manipulators. These engines also differ in their support for reinforcement learning, batched simulation, contact-rich physics, photorealistic rendering, and sensor simulation. Refer to Figure 2 for a categorization of simulation engines by robot domain and acceleration profile.
Developers often struggle with which simulation engine to use for the use case they are tackling. Oftentimes, this falls on a few fundamental questions, such as These factors would help developers in deciding which simulation engine to use for their development needs. NVIDIA Isaac Sim and Isaac Lab, MuJoCo, PyBullet, Drake, and Genesis are some of the most popular simulation engines for humanoids and two-arm dexterous manipulation.
Why it matters
Large language models (LLMs) and vision-language models (VLMs) can be trained on internet-scale datasets, but robotics and physical AI systems do not have the same advantage. To train a physical AI system, a robot must learn the consequences of interacting with the physical world. For example, it needs to understand what happens when a cup slips, a cable bends, or a gripper contacts an object at the wrong angle.
Collecting this kind of data in the real world is slow, expensive, risky, and sometimes impractical due to the destructive nature of the tasks. Simulation provides a bridge by enabling developers to generate large amounts of photorealistic, physically grounded data. By teleoperating robots in simulation and scaling data collection through GPU parallelism, developers can generate thousands of hours of robot experience at a fraction of the cost of real-world collection.
Earlier robotics simulators were often used primarily to debug geometry, test controllers, or visualize robot motion. Today, simulation has become part of the model development loop. Teams use it to generate perception datasets, train reinforcement learning policies, collect demonstrations, augment real-world data, benchmark models, and test policies against rare or adversarial scenarios.
This shift is why industrial research labs and academic groups are increasingly contributing to, or developing, simulation engines that can meet these new requirements. Figure 2: A physical system (Earth and robot) continuously exchanges data with its virtual representation (digital model), enabling monitoring, analysis, prediction, and control through a bidirectional feedback loop. Each computer plays a different role depending on the task’s latency, throughput, accuracy, and deployment requirements.
What to watch
Each of these simulation engines is unique to specific use cases such as scaling reinforcement learning, generating synthetic data, testing algorithms and/or models in simulation. Here we will provide a quick overview of MuJoCo, MuJoCo Warp, NVIDIA Isaac Sim, and Isaac Lab. MuJoCo, short for Multi-Joint dynamics with Contact, is a fast, accurate, open-source physics engine built mainly for robotics, biomechanics, reinforcement learning, control, and simulation of articulated systems.
Unlike engines focused mostly on games or visual realism, MuJoCo is designed around precise dynamics, contact-rich motion, and model-based optimization. MuJoCo emphasizes speed, accuracy, and modeling power for systems where physical correctness matters. Figure 3: Robotic manipulator simulated in MuJoCo. The figure illustrates a multi-joint robotic arm with a parallel grippe