get_environment not defined for examples

[richardrl]

I am just trying to run block.jl and get this:

  Activating project at `~/Dojo.jl/examples`
ERROR: LoadError: UndefVarError: get_environment not defined
Stacktrace:
 [1] top-level scope
   @ ~/Dojo.jl/examples/trajectory_optimization/block.jl:13
in expression starting at /root/Dojo.jl/examples/trajectory_optimization/block.jl:13

 *  The terminal process "julia '--color=yes', '--project=/root/.julia/environments/v1.8', '/root/Dojo.jl/examples/trajectory_optimization/block.jl'" terminated with exit code: 1. 
 *  Terminal will be reused by tasks, press any key to close it. 

 *  Executing task: julia --color=yes --project=/root/.julia/environments/v1.8 /root/Dojo.jl/examples/trajectory_optimization/block.jl 

  Activating project at `~/Dojo.jl/examples`
ERROR: LoadError: UndefVarError: get_environment not defined
Stacktrace:
 [1] top-level scope
   @ ~/Dojo.jl/examples/trajectory_optimization/block.jl:13
in expression starting at /root/Dojo.jl/examples/trajectory_optimization/block.jl:13

 *  The terminal process "julia '--color=yes', '--project=/root/.julia/environments/v1.8', '/root/Dojo.jl/examples/trajectory_optimization/block.jl'" terminated with exit code: 1. 
 *  Terminal will be reused by tasks, press any key to close it. 

I installed Dojo, and also DojoEnvironments using the package manager. How can I fix this?

[richardrl]

I had to explicitly import DojoEnvironments and also call DojoEnvironments.visualize explicitly.

It runs now, but fails.

  Activating project at `~/Dojo.jl/examples`
┌ Info: MeshCat server started. You can open the visualizer by visiting the following URL in your browser:
└ http://127.0.0.1:8702
 20.840603 seconds (284.38 M allocations: 31.481 GiB, 18.57% gc time, 36.06% compilation time)
IterativeLQR.eval_obj(s.m_data.obj.costs, s.m_data.x, s.m_data.u, s.m_data.w) = NaN
s.s_data.iter[1] = 15
norm(goal(s.m_data.x[T], zeros(0), zeros(0)), Inf) = NaN
 *  Terminal will be reused by tasks, press any key to close it. 

Modified block.jl:

using Pkg
Pkg.activate(joinpath(@__DIR__, ".."))
Pkg.instantiate()

# ## setup
using Dojo
using IterativeLQR
using LinearAlgebra 
using DojoEnvironments
import DojoEnvironments

# ## system
gravity = -9.81
timestep = 0.1
env = get_environment(:block, 
    representation=:maximal, 
    timestep=timestep,
    friction_coefficient=0.5,
    gravity=gravity);

env.mechanism.contacts[1].model.friction_coefficient = 10.0
# ## visualizer 
render(env.vis) 

# ## dimensions
n = env.num_states
m = env.num_inputs

# ## states
z1 = [0.0; 0.0; 0.25; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0]
zT = [0.0; 1.0; 0.25; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0] # right goal
## zT = [0.0; 0.0; 0.25 + 1.0; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0] # up goal

# ## horizon
T = 11

# ## model
dyn = IterativeLQR.Dynamics(
    (y, x, u, w) -> dynamics(y, env, x, u, w), 
    (dx, x, u, w) -> dynamics_jacobian_state(dx, env, x, u, w, attitude_decompress=true),
    (du, x, u, w) -> dynamics_jacobian_input(du, env, x, u, w, attitude_decompress=true),
    n, n, m)
model = [dyn for t = 1:T-1];

# ## rollout
ū = [[0.0; 0.0; 0.0] for t = 1:T-1]
x̄ = rollout(model, z1, ū)
DojoEnvironments.visualize(env, x̄);

# ## objective
ot = (x, u, w) -> transpose(x - zT) * Diagonal(1.0 * ones(n)) * (x - zT) + transpose(u) * Diagonal(1.0e-2 * ones(m)) * u
oT = (x, u, w) -> transpose(x - zT) * Diagonal(1.0 * ones(n)) * (x - zT)

ct = Cost(ot, n, m)
cT = Cost(oT, n, 0)
obj = [[ct for t = 1:T-1]..., cT];

# ## constraints
goal(x, u, w) = x - zT

cont = IterativeLQR.Constraint()
conT = IterativeLQR.Constraint(goal, n, 0)
cons = [[cont for t = 1:T-1]..., conT];

# ## solver
s = IterativeLQR.solver(model, obj, cons, 
    opts=Options(
        linesearch=:armijo,
        α_min=1.0e-5,
        obj_tol=1.0e-3,
        grad_tol=1.0e-3,
        con_tol=0.005,
        max_iter=100,
        max_al_iter=10,
        ρ_init=1.0,
        ρ_scale=10.0,
        verbose=false))
IterativeLQR.initialize_controls!(s, ū)
IterativeLQR.initialize_states!(s, x̄);

# ## solve
@time IterativeLQR.solve!(s);

# ## solution
z_sol, u_sol = IterativeLQR.get_trajectory(s)
@show IterativeLQR.eval_obj(s.m_data.obj.costs, s.m_data.x, s.m_data.u, s.m_data.w)
@show s.s_data.iter[1]
@show norm(goal(s.m_data.x[T], zeros(0), zeros(0)), Inf)

# ## visualize
z_vis = [[z_sol[1] for t = 1:10]..., z_sol..., [z_sol[end] for t = 1:10]...]
u_vis = [[u_sol[1] for t = 1:10]..., u_sol..., [u_sol[end] for t = 1:10]...]
vis, anim = DojoEnvironments.visualize(env, z_vis)
vis, anim = DojoEnvironments.visualize_block_force!(vis, anim, z_vis, u_vis) 

set_camera!(env.vis, 
    zoom=50.0, 
    cam_pos=[100.0, 0.0, 0.0]);
    
set_floor!(env.vis, 
    x=0.0, 
    y=4.0, 
    z=0.02, 
    color=RGBA(0.7, 0.7, 0.7, 1.0));

[richardrl]

Also, the jupyter notebook generated with Literate.jl also NAN at the same place with no visualization for block.jl

[thowell]

I just pushed a fix for the block example. Used explicit package dependencies for some of the methods, reduced the friction coefficient and changed one of the optimizer settings so that the example works. We will improve this example further, but you should be able to see a result now.

Note, at some point we changed the inputs from impulses to forces, so I think the scaling is off--we need to fix this.

[janbruedigam]

Most environments have been removed in the latest version, but the mechanisms still exist and can be created with, for example, get_mechanism(:block).