Comments (4)
Hi @ajordana,
Thanks for telling us about this limitation in our Python bindings.
You can change the lower/upper bounds on inequality constraints using this #1171.
I'll close this issue, but feel free to ask any questions.
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Thank you very much @cmastalli! I was able to create a constrained LQR by inheriting from ActionModelAbstract
.
However, I noticed the following: when I create a constrained LQR by inheriting from DifferentialActionModelAbstract
, I am able to set g_ub
and g_lb
but it seems that the Euler integration resets them to their default value.
Here is an example:
import numpy as np
import crocoddyl
class DifferentialActionModelLQR(crocoddyl.DifferentialActionModelAbstract):
def __init__(self):
nx = 4
nu = 2
nr = 1
ng = 4
nh = 0
state = crocoddyl.StateVector(nx)
crocoddyl.DifferentialActionModelAbstract.__init__(self, state, nu, nr, ng, nh)
self.g_lb = np.array([-np.inf] * ng)
self.g_ub = np.array([10.0] * ng)
def calc(self, data, x, u=None):
pass
def calcDiff(self, data, x, u=None):
pass
lqr_diff = DifferentialActionModelLQR()
print("Differential model g_ub = ", lqr_diff.g_ub)
dt = 0.01
lqr = crocoddyl.IntegratedActionModelEuler(lqr_diff, dt)
print("Integrated model g_ub = ", lqr.g_ub)
Is there something wrong in what I am doing?
from crocoddyl.
@ajordana -- please see #1180. It should fix your issue.
from crocoddyl.
Yes, this fixes the issue. Thank you!
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