IterativeLQR.jl

A Julia package for solving constrained trajectory optimization problems with iterative LQR (iLQR).

$$ \begin{align*} \underset{x_{1:T}, \phantom{,} u_{1:T-1}}{\text{minimize }} & \phantom{,} g_T(x_T; \theta_T) + \sum \limits_{t = 1}^{T-1} g_t(x_t, u_t; \theta_t)\\ \text{subject to } & \phantom{,} f_t(x_t, u_t; \theta_t) = x_{t+1}, \phantom{,} \quad t = 1,\dots,T-1,\\ & \phantom{,} c_t(x_t, u_t; \theta_t) \phantom{,}[\leq, =] \phantom{,} 0, \quad t = 1, \dots, T,\\ \end{align*} $$

with

• $x_{1:T}$: state trajectory

• $u_{1:T-1}$: action trajectory

• $\theta_{1:T}$: problem-data trajectory

• Fast and allocation-free gradients and Jacobians are automatically generated using Symbolics.jl for user-provided costs, constraints, and dynamics.

• Constraints are handled using an augmented Lagrangian framework.

• Cost, dynamics, and constraints can have varying dimensions at each time step.

• Parameters are exposed (and gradients wrt these values coming soon!)

For more details, see our related paper: ALTRO: A Fast Solver for Constrained Trajectory Optimization

From the Julia REPL, type ] to enter the Pkg REPL mode and run:

pkg> add https://github.com/thowell/IterativeLQR.jl

using IterativeLQR 
using LinearAlgebra

# horizon 
T = 11 

# particle 
num_state = 2
num_action = 1 

function particle_discrete(x, u)
   A = [1.0 1.0; 0.0 1.0]
   B = [0.0; 1.0] 
   return A * x + B * u[1]
end

# model
particle = Dynamics(particle_discrete, num_state, num_action)
model = [particle for t = 1:T-1] 

# initialization
x1 = [0.0; 0.0] 
xT = [1.0; 0.0]
ū = [1.0e-1 * randn(num_action) for t = 1:T-1] 
x̄ = rollout(model, x1, ū)

# objective  
objective = [
   [Cost((x, u) -> 0.1 * dot(x, x) + 0.1 * dot(u, u), num_state, num_action) for t = 1:T-1]..., 
   Cost((x, u) -> 0.1 * dot(x, x), num_state, 0)
]

# constraints
constraints = [
   [Constraint() for t = 1:T-1]..., 
   Constraint((x, u) -> x - xT, num_state, 0)
] 

# solver
solver = Solver(model, objective, constraints)
initialize_controls!(solver, ū)
initialize_states!(solver, x̄)

# solve
solve!(solver)

# solution
x_sol, u_sol = get_trajectory(solver)

Please see the following for examples using this package:

• Trajectory Optimization with Optimization-Based Dynamics
• Dojo: A Differentiable Simulator for Robotics