Skip to content

Instantly share code, notes, and snippets.

🎯
Focusing

Rishabh Jangir jangirrishabh

🎯
Focusing
Block or report user

Report or block jangirrishabh

Hide content and notifications from this user.

Learn more about blocking users

Contact Support about this user’s behavior.

Learn more about reporting abuse

Report abuse
View GitHub Profile
View cloth_place.py
"""
Scene file for cloth on table.
To check deformations of the cloth under Attach Constraint and Fixed Constraint
Current script uses only Attach constraint at the 2 red balls to grasp the cloth
To add Fixed constraint uncomment line 62
Note: The red balls are supposed to move together in simulation and thus the cloth must follow them with minimum deformations
at the grasped vertices
"""
View workspace.py
class workspace (Sofa.PythonScriptController):
def __init__(self, node, commandLineArguments) :
self.commandLineArguments = commandLineArguments
print "Command line arguments for python : "+str(commandLineArguments)
self.node = node
self.createGraph(node)
return None;
def createGraph(self,rootNode):
View ball_collision_testing.scn
<?xml version="1.0" ?>
<!-- Testing ball collision problem from hugo -->
<Node name="root" dt="0.05" gravity="0 -9.810 0">
<VisualStyle displayFlags="hideBehaviorModels hideCollisionModels hideMappings hideForceFields showVisualModels hideInteractionForceFields" />
<RequiredPlugin name='SofaMiscCollision'/>
<RequiredPlugin name="SofaPython" pluginName="SofaPython" />
<FreeMotionAnimationLoop solveVelocityConstraintFirst="0" />
<LCPConstraintSolver maxIt="1000" tolerance="1e-6" initial_guess="false" build_lcp="true" multi_grid="false" printLog="0" mu="0.000000000001"/>
View ball_collision.scn
<Node name="root" dt="0.05" gravity="0 -9.810 0">
<VisualStyle displayFlags="hideBehaviorModels hideCollisionModels hideMappings hideForceFields showVisualModels hideInteractionForceFields" />
<RequiredPlugin name='SofaMiscCollision'/>
<RequiredPlugin name="SofaPython" pluginName="SofaPython" />
<FreeMotionAnimationLoop solveVelocityConstraintFirst="0" />
<LCPConstraintSolver maxIt="1000" tolerance="1e-6" initial_guess="false" build_lcp="true" multi_grid="false" printLog="0" mu="1.9"/>
<CollisionPipeline depth="6" verbose="0" draw="0" />
<BruteForceDetection name="N2" />
View cloth_on_table_no_friciton.scn
<Node name="root" dt="0.05" gravity="0 -9.810 0">
<VisualStyle displayFlags="hideBehaviorModels hideCollisionModels hideMappings hideForceFields showVisualModels" />
<RequiredPlugin name='SofaMiscCollision'/>
<LCPConstraintSolver maxIt="1000" tolerance="1e-6" initial_guess="false" build_lcp="true" multi_grid="false" printLog="0" mu="0.9"/>
<FreeMotionAnimationLoop solveVelocityConstraintFirst="1" />
<CollisionPipeline depth="15" verbose="0" draw="0" />
<BruteForceDetection name="N2" />
<LocalMinDistance name="Proximity" alarmDistance="0.3" contactDistance="0.1" useLMDFilters="0" />
<CollisionResponse name="Response" response="FrictionContact" />
<DiscreteIntersection/>
View keyboardControl.py
import Sofa
class KeyboardControl(Sofa.PythonScriptController):
# called once graph is created, to init some stuff...
def initGraph(self,node):
print 'initGraph called (python side)'
self.MechanicalState = node.getObject('DOFs')
gravity = node.findData('gravity').value
print gravity
self.rootNode = node.getRoot()
@jangirrishabh
jangirrishabh / toyCarIRL2.py
Created Jun 14, 2018
Snippet for toyCarIRL, blog usage, not executable
View toyCarIRL2.py
def optimalWeightFinder(self):
i = 1
while True:
W = self.optimization() # optimize to find new weights in the list of policies
print ("weights ::", W )
print ("the distances ::", self.policiesFE.keys())
self.currentT = self.policyListUpdater(W, i)
print ("Current distance (t) is:: ", self.currentT )
if self.currentT <= self.epsilon: # terminate if the point reached close enough
break
@jangirrishabh
jangirrishabh / toyCarIRL3.py
Created Jun 14, 2018
Snippet for toyCarIRL, blog usage, not executable
View toyCarIRL3.py
class irlAgent:
def __init__(self, randomFE, expertFE, epsilon, num_states, num_frames, behavior):
self.randomPolicy = randomFE
self.expertPolicy = expertFE
self.num_states = num_states
self.num_frames = num_frames
self.behavior = behavior
self.epsilon = epsilon # termination when t < 0.1
self.randomT = np.linalg.norm(np.asarray(self.expertPolicy)-np.asarray(self.randomPolicy)) #norm of the diff in expert and random
self.policiesFE = {self.randomT:self.randomPolicy} # storing the policies and their respective t values in a dictionary
@jangirrishabh
jangirrishabh / toyCarIRL4.py
Created Jun 14, 2018
Snippet for toyCarIRL, blog usage, not executable
View toyCarIRL4.py
def getRLAgentFE(self, W, i): #get the feature expectations of a new poliicy using RL agent
IRL_helper(W, self.behavior, self.num_frames, i) # train the agent and save the model in a file used below
saved_model = 'saved-models_'+self.behavior+str(i)+'/164-150-100-50000-'+str(self.num_frames)+'.h5' # use the saved model to get the FE
model = neural_net(self.num_states, [164, 150], saved_model)
return play(model, W)#return feature expectations by executing the learned policy
@jangirrishabh
jangirrishabh / toyCarIRL4.py
Created Jun 14, 2018
Snippet for toyCarIRL, blog usage, not executable
View toyCarIRL4.py
def policyListUpdater(self, W, i): #add the policyFE list and differences
tempFE = self.getRLAgentFE(W, i) # get feature expectations of a new policy respective to the input weights
hyperDistance = np.abs(np.dot(W, np.asarray(self.expertPolicy)-np.asarray(tempFE))) #hyperdistance = t
self.policiesFE[hyperDistance] = tempFE
return hyperDistance # t = (weights.tanspose)*(expert-newPolicy)
You can’t perform that action at this time.