From fd793e95a2de7e460a67ffddc84fad2328662a1e Mon Sep 17 00:00:00 2001
From: odri <odri@furano.laas.fr>
Date: Tue, 16 Aug 2022 12:15:10 +0200
Subject: [PATCH] more cost on not movig feet more amping
---
config/walk_parameters.yaml | 10 +++++-----
.../quadruped_reactive_walking/WB_MPC/ProblemData.py | 4 ++--
2 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/config/walk_parameters.yaml b/config/walk_parameters.yaml
index 393ca060..c4dbffaf 100644
--- a/config/walk_parameters.yaml
+++ b/config/walk_parameters.yaml
@@ -6,12 +6,12 @@ robot:
LOGGING: true # Enable/disable logging during the experiment
PLOTTING: true # Enable/disable automatic plotting at the end of the experiment
DEMONSTRATION: false # Enable/disable demonstration functionalities
- SIMULATION: true # Enable/disable PyBullet simulation or running on real robot
+ SIMULATION: false # Enable/disable PyBullet simulation or running on real robot
enable_pyb_GUI: true # Enable/disable PyBullet GUI
envID: 0 # Identifier of the environment to choose in which one the simulation will happen
use_flat_plane: true # If True the ground is flat, otherwise it has bumps
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
+ N_SIMULATION: 15000 # Number of simulated wbc time steps
enable_corba_viewer: false # Enable/disable Corba Viewer
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
@@ -24,7 +24,7 @@ robot:
dt_wbc: 0.001 # Time step of the whole body control
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: true # true to interpolate the impedance quantities between nodes of the MPC
+ save_guess: false # true to interpolate the impedance quantities between nodes of the MPC
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
@@ -32,12 +32,12 @@ robot:
# Kd_main: [0., 0., 0.] # Derivative gains for the PD+
# Kff_main: 0.0 # Feedforward torques multiplier for the PD+
Kp_main: [3, 3, 3] # Proportional gains for the PD+
- Kd_main: [0.3, 0.3, 0.3] # Derivative gains for the PD+
+ Kd_main: [0.5, 0.5, 0.5] # Derivative gains for the PD+
Kff_main: 1.0 # Feedforward torques multiplier for the PD+
# Parameters of Gait
N_periods: 1
- gait: [100, 0, 0, 0, 0] # Initial gait matrix
+ gait: [80, 0, 0, 0, 0] # Initial gait matrix
# Parameters of Joystick
gp_alpha_vel: 0.003 #Â Coefficient of the low pass filter applied to gamepad velocity
diff --git a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
index dfcd025a..c10c293f 100644
--- a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
+++ b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
@@ -175,8 +175,8 @@ class ProblemDataFull(problemDataAbstract):
self.control_bound_w = 1e3
self.control_reg_w = 1e2
self.state_reg_w = np.array(
- [1e1] * 3 + [1e-2] * 3 + [1e1] * 6 +
- [1e1] * 3 + [1e0] * 3 + [1e1] * 6
+ [1e2] * 3 + [1e-2] * 3 + [1e2] * 6 +
+ [1e1] * 3 + [1e-0] * 3 + [1e1] * 6
)
self.terminal_velocity_w = np.array([0] * 12 + [1e3] * 12)
--
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