Quickstart Examples

This section contains practical examples for each backend. Choose based on your use case:

• NumPy – Simple numerical computation, validation

• JAX – Automatic differentiation, GPU, native batching

• CasADi – Symbolic computation, optimization formulation, automatic differentiation

• PyTorch – GPU acceleration, native batching, ML integration, automatic differentiation

• NumPy Backend
  • Basic Usage
  • Common Operations
  • Loading from MuJoCo
  • When to Use NumPy
• JAX Backend
  • Key Features
  • Basic Usage
  • JIT Compilation
  • Automatic Differentiation
  • Native Batching
  • Optimization Example
  • Tips and Tricks
  • Loading from MuJoCo
  • When to Use JAX
• CasADi Backend
  • Key Features
  • Basic Usage
  • Function Variants
  • Symbolic Computation
  • Optimization Problem
  • Inverse Kinematics
  • Tips and Tricks
  • Loading from MuJoCo
  • When to Use CasADi
• PyTorch Backend
  • Key Features
  • Basic Usage
  • GPU Support
  • Automatic Differentiation
  • Common Operations
  • Learning-Based Control
  • Batch Processing
  • Batch Use Cases
  • Batch Tips and Tricks
  • Performance Tips
  • Loading from MuJoCo
  • When to Use PyTorch