From ec2160b291a80e7eeb09068de17ab8b7a0ecdba4 Mon Sep 17 00:00:00 2001
From: Ale <alessandroassirell98@gmail.com>
Date: Fri, 12 Aug 2022 12:18:28 +0200
Subject: [PATCH] still have to work on multiprocessing
---
config/walk_parameters.yaml | 4 ++--
python/quadruped_reactive_walking/WB_MPC/ProblemData.py | 6 +++---
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/config/walk_parameters.yaml b/config/walk_parameters.yaml
index 9f0b7b3c..b11e42aa 100644
--- a/config/walk_parameters.yaml
+++ b/config/walk_parameters.yaml
@@ -13,7 +13,7 @@ robot:
predefined_vel: true # If we are using a predefined reference velocity (True) or a joystick (False)
N_SIMULATION: 10000 # Number of simulated wbc time steps
enable_corba_viewer: false # Enable/disable Corba Viewer
- enable_multiprocessing: false # Enable/disable running the MPC in another process in parallel of the main loop
+ enable_multiprocessing: true # Enable/disable running the MPC in another process in parallel of the main loop
perfect_estimator: true # Enable/disable perfect estimator by using data directly from PyBullet
# General control parameters
@@ -25,7 +25,7 @@ robot:
dt_mpc: 0.015 # Time step of the model predictive control
type_MPC: 3 # Which MPC solver you want to use: 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
save_guess: false # true to interpolate the impedance quantities between nodes of the MPC
- movement: "circle" # name of the movement to perform
+ movement: "step" # name of the movement to perform
interpolate_mpc: true # true to interpolate the impedance quantities between nodes of the MPC
interpolation_type: 3 # 0,1,2,3 decide which kind of interpolation is used
# Kp_main: [0.0, 0.0, 0.0] # Proportional gains for the PD+
diff --git a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
index c8ecf039..56541ea0 100644
--- a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
+++ b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
@@ -175,12 +175,12 @@ class ProblemDataFull(problemDataAbstract):
self.foot_tracking_w = 1e4
# self.friction_cone_w = 1e3 * 0
self.control_bound_w = 1e3
- self.control_reg_w = 1e0
+ self.control_reg_w = 1e-1
self.state_reg_w = np.array([1e1] * 3
- + [1e-1] * 3
+ + [1e-2] * 3
+ [1e1] * 6
+ [1e1] * 3
- + [1e0] * 3
+ + [3*1e-1] * 3
+ [1e1] * 6
)
self.terminal_velocity_w = np.array([0] * 12 + [1e3] * 12)
--
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