Rename minimise_τ to minimise_torques

Author: ferrolho

interoperability/python_calls_julia.py

@@ -120,7 +120,7 @@ def main():
             initial_guess=jl_initial_guess,
             user_options=ipopt_options,
             use_inv_dyn=True,
-            minimise_τ=False,
+            minimise_torques=False,
         )
 
         # Unpack the solution `x` into joint positions, velocities, and torques

notebooks/Example.ipynb

@@ -153,7 +153,7 @@
    "outputs": [],
    "source": [
     "use_inv_dyn = true\n",
-    "minimise_τ = false\n",
+    "minimise_torques = false\n",
     "\n",
     "user_options = Dict(\n",
     "    # === Termination === #  https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Termination\n",
@@ -202,7 +202,7 @@
     "cpu_time, x, solver_log = solve(problem, robot,\n",
     "                                initial_guess=initial_guess,\n",
     "                                use_inv_dyn=use_inv_dyn,\n",
-    "                                minimise_τ=minimise_τ,\n",
+    "                                minimise_torques=minimise_torques,\n",
     "                                user_options=user_options)"
    ]
   },
@@ -238,15 +238,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.11.1",
+   "display_name": "Julia 1.11.3",
    "language": "julia",
    "name": "julia-1.11"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.11.1"
+   "version": "1.11.3"
   }
  },
  "nbformat": 4,

notebooks/Tutorial.ipynb

@@ -128,15 +128,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.11.1",
+   "display_name": "Julia 1.11.3",
    "language": "julia",
    "name": "julia-1.11"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.11.1"
+   "version": "1.11.3"
   }
  },
  "nbformat": 4,

src/TORA.jl

@@ -24,7 +24,7 @@ import Base: length
     solve_with_knitro(problem, robot;
                       initial_guess=Float64[],
                       use_inv_dyn=false,
-                      minimise_τ=false,
+                      minimise_torques=false,
                       user_options=Dict())
 
 Solve the nonlinear optimization problem with Knitro.
@@ -34,7 +34,7 @@ Further options can be set using the keyword arguments. See [Solver Interfaces](
 # Keyword arguments
 - `initial_guess::Vector{Float64}=Float64[]`: the starting point for the solver.
 - `use_inv_dyn::Bool=false`: if true, enables the use of inverse dynamics instead of forward dynamics.
-- `minimise_τ::Bool=false`: if true, activates a cost function to minimize the joint torques.
+- `minimise_torques::Bool=false`: if true, activates a cost function to minimize the joint torques.
 - `user_options::Dict=Dict()`: the user options for Knitro.
 
 See also: [`solve_with_ipopt`](@ref)

src/precompile.jl

@@ -45,9 +45,9 @@ using PrecompileTools: @setup_workload, @compile_workload
 
         initial_guess = zeros(problem.num_knots * (robot.n_q + robot.n_v + robot.n_τ) - robot.n_τ)
 
-        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=false, minimise_τ=false)
-        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=false, minimise_τ=true)
-        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=true, minimise_τ=false)
+        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=false, minimise_torques=false)
+        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=false, minimise_torques=true)
+        cpu_time, x, solver_log = solve_with_ipopt(problem, robot, initial_guess=initial_guess, user_options=ipopt_options, use_inv_dyn=true, minimise_torques=false)
 
         play_trajectory(vis, problem, robot, x)
         plot_results(problem, robot, x)

src/transcription/ipopt.jl

@@ -6,7 +6,7 @@ addOption(prob, k, v::Float64) = Ipopt.AddIpoptNumOption(prob, k, v)
     solve_with_ipopt(problem, robot;
                      initial_guess=Float64[],
                      use_inv_dyn=false,
-                     minimise_τ=false,
+                     minimise_torques=false,
                      user_options=Dict())
 
 Solve the nonlinear optimization problem with Ipopt.
@@ -16,15 +16,15 @@ Further options can be set using the keyword arguments. See [Solver Interfaces](
 # Keyword arguments
 - `initial_guess::Vector{Float64}=Float64[]`: the starting point for the solver.
 - `use_inv_dyn::Bool=false`: if true, enables the use of inverse dynamics instead of forward dynamics.
-- `minimise_τ::Bool=false`: if true, activates a cost function to minimize the joint torques.
+- `minimise_torques::Bool=false`: if true, activates a cost function to minimize the joint torques.
 - `user_options::Dict=Dict()`: the user options for Ipopt.
 
 See also: [`solve_with_knitro`](@ref)
 """
 function solve_with_ipopt(problem::Problem, robot::Robot;
                           initial_guess::Vector{Float64}=Float64[],
                           use_inv_dyn::Bool=false,
-                          minimise_τ::Bool=false,
+                          minimise_torques::Bool=false,
                           user_options::Dict=Dict("hessian_approximation" => "limited-memory"))
     # # # # # # # # # # # # # # # #
     # Variables and their bounds  #
@@ -283,7 +283,7 @@ function solve_with_ipopt(problem::Problem, robot::Robot;
         grad_f[:] = zeros(n)
     end
 
-    if minimise_τ
+    if minimise_torques
         ind_τ = hcat([range(1 + (i * nₓ) + robot.n_q + robot.n_v, length=robot.n_τ)
                       for i = (1:problem.num_knots - 1) .- 1]...)
         indexVars, coefs = vec(ind_τ), fill(1 / (problem.num_knots - 1), n3)

src/transcription/knitro.jl

@@ -3,7 +3,7 @@ using .KNITRO
 function solve_with_knitro(problem::Problem, robot::Robot;
                            initial_guess::Vector{Float64}=Float64[],
                            use_inv_dyn::Bool=false,
-                           minimise_τ::Bool=false,
+                           minimise_torques::Bool=false,
                            user_options::Dict=Dict())
     lm = KNITRO.LMcontext()  # Instantiate license manager
     kc = KNITRO.KN_new_lm(lm)  # Create a new Knitro instance
@@ -136,7 +136,7 @@ function solve_with_knitro(problem::Problem, robot::Robot;
     # Objective #
     # # # # # # #
 
-    if minimise_τ
+    if minimise_torques
         # Minimize τ, i.e., the necessary joint torques.
         indexVars, coefs = Cint.(vec(ind_τ) .- 1), fill(1 / (problem.num_knots - 1), n3)
         KNITRO.KN_add_obj_quadratic_struct(kc, indexVars, indexVars, coefs)