PyBullet

PyBullet is a Python wrapper based on the Bullet Physics SDK, developed and maintained by Erwin Coumans, and was initially promoted by the Google Brain team for reinforcement learning research.

PyBullet is a Python wrapper for the Bullet physics engine, lightweight and easy to use, commonly used for teaching and rapid prototype development.

PyBullet is an open-source physics simulation engine capable of simulating rigid body dynamics, collision detection, and sensor feedback

The researcher uses two open-source tools, Stable Baselines3 and PyBullet, for drone reinforcement learning

Developers typically first train models in physics simulation environments (such as Isaac Gym, MuJoCo, or PyBullet).

Mainstream physics engines such as MuJoCo, Isaac Gym, and PyBullet differ considerably in contact modeling, joint friction, and rigid-body dynamics.

Mainstream physics engines include MuJoCo (high-precision contact dynamics), PyBullet (open-source lightweight), Isaac Gym (GPU-parallel acceleration), and Gazebo (ROS ecosystem integration), among others.

PyBullet's physical fidelity is somewhat compromised compared to Gazebo, and its sensor modeling is not sufficiently rich

gym-pybullet-drones is an open-source simulation environment built specifically for drone reinforcement learning, based on the PyBullet physics engine

The same hill-climbing task requires 1 hour and 45 minutes to complete training on PyBullet with a CPU.

For an undergraduate thesis drone MARL project, PyBullet is more suitable because of its gentle learning curve, low hardware requirements (runs smoothly on an RTX 4060), and mature community, enabling the team to enter the algorithm development phase more quickly.

Training with on-policy algorithms such as PPO typically requires 10 million to 50 million steps, and for a scale of 3–5 drones, PyBullet's CPU serial simulation speed is already sufficient.

The Bullet engine used by PyBullet employs a Sequential Impulse solver to handle rigid-body contacts and constraints, advancing the simulation at a fixed timestep (typically 240 Hz, approximately 4.17 ms).

For projects involving 3–5 drones, PyBullet's serial simulation speed on CPU is sufficient to support the training requirements of on-policy algorithms such as PPO, and the acceleration advantage of GPU parallelization is not significant.

For undergraduate graduation projects, it is recommended to first complete core algorithm validation with PyBullet, and attempt migration to Isaac Lab for comparative experiments only if time permits.

For simple tasks such as hovering and basic coordination, PyBullet and Isaac Lab deliver comparable performance.

For an undergraduate thesis, choosing Isaac Lab may extend the environment setup phase to 4–5 weeks, severely compressing time for subsequent algorithm development; PyBullet allows teams to enter the algorithm development phase more quickly.

gym-pybullet-drones is an open-source drone simulation environment based on the PyBullet physics engine, developed and maintained by a team at the University of Toronto, and is compatible with the OpenAI Gymnasium interface standard.

PyBullet uses a CPU-side rigid-body dynamics solver, whose physics accuracy is inferior to that of GPU-accelerated PhysX 5.

Sim-to-Real technology generates synthetic data at scale in physical simulation engines such as NVIDIA Isaac Sim, MuJoCo, and PyBullet, and uses domain randomization and domain adaptation techniques to bridge the gap between simulation and reality.