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removed extra planner arg in demo.launch
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omid1366
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Aug 2, 2019
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trajopt_param: | ||
improve_ratio_threshold: 0.25 # minimum ratio true_improve/approx_improve to accept step. [Research Paper] TrueImprove/ModelImprove > c | ||
min_trust_box_size: 1e-4 # if trust region gets any smaller, exit and report convergence. [Research Paper] xtol | ||
min_approx_improve: 1e-4 # if model improves less than this, exit and report convergence | ||
min_approx_improve_frac: -.Inf # if model improves less than this, exit and report convergence | ||
max_iter: 100 # The max number of iterations | ||
trust_shrink_ratio: 0.1 # if improvement is less than improve_ratio_threshold, shrink trust region by this ratio. [Research Paper] tao- | ||
trust_expand_ratio: 1.5 # if improvement is less than improve_ratio_threshold, shrink trust region by this ratio. [Research Paper] tao+ | ||
cnt_tolerance: 1e-4 # after convergence of penalty subproblem, if constraint violation is less than this, we're done. [Research Paper] ctol | ||
max_merit_coeff_increases: 5 # number of times that we jack up penalty coefficient. [Reasearch Paper] Max iteration in PenaltyIteration loop | ||
merit_coeff_increase_ratio: 10 # ratio that we increate coeff each time. [Researcy Paper] Penalty scaling factory (italic kappa) | ||
max_time: .inf # not yet implemented | ||
merit_error_coeff: 10 # initial penalty coefficient. [Research Paper] mu_0 | ||
trust_box_size: 1e-1 # current size of trust region (component-wise). [Research Paper] s | ||
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problem_info: | ||
basic_info: | ||
n_steps: 20 # 2 * steps_per_phase | ||
dt_upper_lim: 2.0 # The upper limit of 1/dt values allowed in the optimization | ||
dt_lower_lim: 100.0 # The lower limit of 1/dt values allowed in the optimization | ||
start_fixed: true # if true, start pose is the current pose at timestep = 0 | ||
# if false, the start pose is the one given by req.start_state | ||
use_time: flase # if false, it means the timestep is unit, meaning x1-x0 is the velocity for example | ||
# if true, then cost_infos ro cnt_infos should have TT_USE_TIME for their term_type | ||
convex_solver: 1 # which convex solver to use | ||
# 1 = AUTO_SOLVER, 2 = BPMPD, 3 = OSQP, 4 = QPOASES, 5 = GUROBI | ||
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init_info: | ||
type: 3 # 1 = STATIONARY, 2 = JOINT_INTERPOLATED, 3 = GIVEN_TRAJ | ||
# request.start_state should be provided for JOINT_INTERPOLATED and GIVEN_TRAJ | ||
dt: 0.5 | ||
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joint_pos_term_info: | ||
start_pos: # from req.start_state | ||
name: start_pos | ||
first_timestep: 10 # First time step to which the term is applied. | ||
last_timestep: 10 # Last time step to which the term is applied. | ||
# if start_fixed is trure then we can not set the first_timestep to 0 for start_pos as it is going | ||
# to be ignored and only the current pose would be the constraint at timestep = 0. | ||
term_type: 2 # 1 = TT_COST, 2 = TT_CNT, 3 = TT_USE_TIME | ||
middle_pos: | ||
name: middle_pos | ||
first_timestep: 15 | ||
last_timestep: 15 | ||
term_type: 2 | ||
goal_pos: | ||
name: goal_pos | ||
first_timestep: 19 | ||
last_timestep: 19 | ||
term_type: 2 |
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