Robotics Simulation

Robotics simulation studies how to represent robot bodies, sensors, environments, and interactions so researchers can test control and learning safely and repeatably before hardware deployment. Approaches range from rigid-body and contact physics engines such as MuJoCo and Isaac Sim, to finite-element models for soft materials, differentiable or learned dynamics, and generated scenes for navigation and manipulation. GPU-parallel reinforcement learning, synthetic demonstrations, digital twins, and simulation-in-the-loop planning help scale training, while benchmarks and hardware-in-the-loop tests assess transfer. The central tension is balancing physical and sensor fidelity with speed and reproducibility: visual realism alone does not ensure correct interactions, and simulated success does not always predict real-world performance without calibration and validation.