Crocoddyl vs iLQR: A Technical Decision Guide for Whole-Body Model Predictive Control

Crocoddyl excels at solving highly nonlinear, constrained optimal control problems because it leverages Differential Dynamic Programming (DDP) with hard constraints. This allows it to handle complex contact dynamics, such as a humanoid robot pushing a heavy object, without violating torque limits. For example, in whole-body Model Predictive Control (MPC) benchmarks, Crocoddyl demonstrates superior convergence in scenarios requiring strict friction cone adherence, often finding feasible trajectories where standard solvers fail due to constraint violation.

iLQR takes a different approach by using a simpler, unconstrained quadratic approximation of the value function. This results in significantly faster per-iteration computation and a lighter code dependency footprint. In practice, for smooth, unconstrained problems like trajectory optimization for a quadrotor or a 7-DoF arm in free space, a standard iLQR implementation can converge in fewer wall-clock milliseconds, making it ideal for high-frequency replanning loops where raw speed trumps complex constraint handling.

The key trade-off: If your priority is strict constraint satisfaction for dynamic, contact-rich manipulation, choose Crocoddyl. If you prioritize computational speed and simplicity for smooth, unconstrained trajectory generation, choose iLQR. Consider Crocoddyl when you need a solver that acts as a rigorous feasibility filter; choose iLQR when you need a lightweight, fast optimizer that can run directly on embedded hardware without heavy library dependencies.

Crocoddyl excels at solving complex, contact-rich whole-body control problems because it leverages a differential dynamic programming (DDP) framework with hard contact constraints. For example, it can generate a trotting gait for a quadruped like Solo in under 1 millisecond per iteration, a feat that standard iLQR struggles with due to the non-smooth dynamics introduced by making and breaking contact. Its abstraction layer allows for rapid prototyping with various cost functions and analytical or finite-difference derivatives, making it a powerful tool for research and development.

A custom iLQR implementation takes a different approach by prioritizing raw computational speed and simplicity for smooth, unconstrained systems. By stripping away the abstraction overhead and hand-optimizing the linear-quadratic subproblems for a specific robot model, a custom iLQR solver can achieve a 20-30% faster solve time per iteration than a general-purpose library like Crocoddyl for tasks like trajectory tracking on a fixed-base manipulator. This results in a leaner codebase with zero external dependencies, which is a significant trade-off for safety-critical, certified systems where every library must be audited.

The key trade-off: If your priority is solving high-dimensional, contact-rich locomotion and manipulation tasks with a flexible, state-of-the-art solver, choose Crocoddyl. Its built-in contact dynamics and efficient DDP solver will drastically reduce your development time. If you prioritize maximum computational efficiency, a minimal dependency footprint, and full code ownership for a well-defined, smooth system like a standard industrial arm, choose a custom iLQR implementation. Consider Crocoddyl when you need to explore novel robot behaviors; choose a custom iLQR when you need to harden a known controller for a production environment.