def main(objectClass, epsilon):
'''Entrypoint to the program.
- Input objectClass: Folder in 3D Net database.
'''
# PARAMETERS =====================================================================================
# objects
randomScale = True
targetObjectAxis = array([0,0,1])
maxAngleFromObjectAxis = 20*(pi/180)
maxObjectTableGap = 0.02
# view
viewCenter = array([0,0,0])
viewKeepout = 0.50
viewWorkspace = [(-1,1),(-1,1),(-1,1)]
# grasps
graspDetectMode = 1 # 0=sample, 1=sample+label
nGraspSamples = 200
graspScoreThresh = 300
nGraspInliers = 3
# learning
weightsFileName = "/home/mgualti/mgualti/PickAndPlace/simulation/caffe/image_iter_5000.caffemodel"
nDataIterations = 300
# visualization/saving
showViewer = False
showEveryStep = False
saveFileName = "results-single-" + objectClass + "-epsilon" + str(epsilon) + ".mat"
# INITIALIZATION =================================================================================
threeDNet = ThreeDNet()
rlEnv = RlEnvironment(showViewer)
rlAgent = RlAgent(rlEnv)
if epsilon < 1.0: rlAgent.LoadNetworkWeights(weightsFileName)
Return = []
# RUN TEST =======================================================================================
# Collect data for this training iteration.
for dataIterationIdx in xrange(nDataIterations):
print("Iteration {}.".format(dataIterationIdx))
# place object in random orientation on table
fullObjName, objScale = threeDNet.GetRandomObjectFromClass(objectClass, randomScale)
objHandle, objRandPose = rlEnv.PlaceObjectRandomOrientation(fullObjName, objScale)
# move the hand to view position and capture a point cloud
cloud, viewPoints, viewPointIndices = rlAgent.GetDualCloud(
viewCenter, viewKeepout, viewWorkspace)
rlAgent.PlotCloud(cloud)
# detect grasps in the sensory data
grasps = rlAgent.DetectGrasps(cloud, viewPoints, viewPointIndices,
nGraspSamples, graspScoreThresh, nGraspInliers, graspDetectMode)
rlAgent.PlotGrasps(grasps)
if len(grasps) == 0:
print("No grasps found. Skipping iteration.")
rlEnv.RemoveObject(objHandle)
rlAgent.UnplotCloud()
continue
if showEveryStep:
raw_input("Press [Enter] to continue...")
# perform pick action
grasp = rlAgent.GetGrasp(grasps, epsilon)
rlAgent.PlotGrasps([grasp])
if showEveryStep:
raw_input("Press [Enter] to continue...")
# perform place action
P = rlAgent.GetPlacePose(grasp, epsilon)
rlAgent.MoveHandToPose(P)
rlAgent.MoveObjectToHandAtGrasp(grasp, objHandle)
r = rlEnv.RewardHeightExponential(
objHandle, targetObjectAxis, maxAngleFromObjectAxis, maxObjectTableGap)
print("The robot receives {} reward.".format(r))
Return.append(r)
if showEveryStep:
raw_input("Press [Enter] to continue...")
# cleanup this data iteration
rlEnv.RemoveObject(objHandle)
rlAgent.UnplotGrasps()
rlAgent.UnplotCloud()
saveData = {"randomScale":randomScale, "targetObjectAxis":targetObjectAxis,
"maxAngleFromObjectAxis":maxAngleFromObjectAxis, "maxObjectTableGap":maxObjectTableGap,
"graspDetectMode":graspDetectMode, "nGraspSamples":nGraspSamples,
"graspScoreThresh":graspScoreThresh, "weightsFileName":weightsFileName,
"nDataIterations":nDataIterations, "epsilon":epsilon, "Return":Return}
savemat(saveFileName, saveData)
def main(saveFileSuffix):
'''Entrypoint to the program.'''
# PARAMETERS =====================================================================================
# objects
objectClass = "mug_train"
randomScale = True
targetObjectAxis = array([0, 0, 1])
maxAngleFromObjectAxis = 20 * (pi / 180)
maxObjectTableGap = 0.03
# view
viewCenter = array([0, 0, 0])
viewKeepout = 0.50
viewWorkspace = [(-1, 1), (-1, 1), (0.002, 1)]
# grasps
graspDetectMode = 0 # 0=sample, 1=sample+label
nGraspSamples = 100
graspScoreThresh = 350
# learning
nValueIterations = 70
nDataIterations = 50
nGraspIterations = 20
pickEpsilon = 1.0
placeEpsilon = 1.0
minPickEpsilon = 0.10
minPlaceEpsilon = 0.10
pickEpsilonDelta = 0.05
placeEpsilonDelta = 0.05
maxExperiences = 25000
trainingBatchSize = 25000
unbiasOnIteration = nValueIterations - 5
# visualization/saving
saveFileName = "results" + saveFileSuffix + ".mat"
recordLoss = True
showViewer = False
showSteps = False
# INITIALIZATION =================================================================================
threeDNet = ThreeDNet()
rlEnv = RlEnvironment(showViewer)
rlAgent = RlAgent(rlEnv)
nPlaceOptions = len(rlAgent.placePoses)
experienceDatabase = []
# RUN TEST =======================================================================================
averageReward = []
placeActionCounts = []
trainLosses = []
testLosses = []
databaseSize = []
iterationTime = []
for valueIterationIdx in xrange(nValueIterations):
print("Iteration {}. Epsilon pick: {}, place: {}".format(\
valueIterationIdx, pickEpsilon, placeEpsilon))
# 1. Collect data for this training iteration.
iterationStartTime = time.time()
R = []
placeCounts = zeros(nPlaceOptions)
# check if it's time to unbias data
if valueIterationIdx >= unbiasOnIteration:
maxExperiences = trainingBatchSize # selects all recent experiences, unbiased
pickEpsilon = 0 # estimating value function of actual policy
placeEpsilon = 0 # estimating value function of actual policy
for dataIterationIdx in xrange(nDataIterations):
# place random object in random orientation on table
fullObjName, objScale = threeDNet.GetRandomObjectFromClass(
objectClass, randomScale)
objHandle, objRandPose = rlEnv.PlaceObjectRandomOrientation(
fullObjName, objScale)
# move the hand to view position and capture a point cloud
cloud, viewPoints, viewPointIndices = rlAgent.GetDualCloud(
viewCenter, viewKeepout, viewWorkspace)
rlAgent.PlotCloud(cloud)
# detect grasps in the sensory data
grasps = rlAgent.DetectGrasps(cloud, viewPoints, viewPointIndices,
nGraspSamples, graspScoreThresh,
graspDetectMode)
rlAgent.PlotGrasps(grasps)
if showSteps:
raw_input("Acquired grasps.")
if len(grasps) == 0:
print("No grasps found. Skipping iteration.")
rlEnv.RemoveObject(objHandle)
rlAgent.UnplotCloud()
continue
for graspIterationIdx in xrange(nGraspIterations):
print("Episode {}.{}.{}.".format(valueIterationIdx,
dataIterationIdx,
graspIterationIdx))
# perform pick action
grasp = rlAgent.GetGrasp(grasps, pickEpsilon)
s = rlEnv.GetState(rlAgent, grasp, None)
rlAgent.PlotGrasps([grasp])
if showSteps:
print("Selected grasp.")
# perform place action
P = rlAgent.GetPlacePose(grasp, placeEpsilon)
rlAgent.MoveHandToPose(P)
ss = rlEnv.GetState(rlAgent, grasp, P)
rlAgent.MoveObjectToHandAtGrasp(grasp, objHandle)
r = rlEnv.RewardBinary(objHandle, targetObjectAxis,
maxAngleFromObjectAxis,
maxObjectTableGap)
print("The robot receives {} reward.".format(r))
if showSteps:
raw_input("Press [Enter] to continue...")
# add experience to database
experienceDatabase.append((s, ss, 0)) # grasp -> placement
experienceDatabase.append((ss, None, r)) # placement -> end
# record save data
R.append(r)
placeCounts += ss[1][len(s[1]) - nPlaceOptions:]
# cleanup this grasp iteration
rlAgent.UnplotGrasps()
rlEnv.MoveObjectToPose(objHandle, objRandPose)
# cleanup this data iteration
rlEnv.RemoveObject(objHandle)
rlAgent.UnplotCloud()
# 2. Compute value labels for data.
experienceDatabase = rlAgent.PruneDatabase(experienceDatabase,
maxExperiences)
Dl = rlAgent.DownsampleAndLabelData(\
experienceDatabase, trainingBatchSize)
databaseSize.append(len(experienceDatabase))
# 3. Train network from replay database.
trainLoss, testLoss = rlAgent.Train(Dl, recordLoss=recordLoss)
trainLosses.append(trainLoss)
testLosses.append(testLoss)
pickEpsilon -= pickEpsilonDelta
placeEpsilon -= placeEpsilonDelta
pickEpsilon = max(minPickEpsilon, pickEpsilon)
placeEpsilon = max(minPlaceEpsilon, placeEpsilon)
# 4. Save results
averageReward.append(mean(R))
placeActionCounts.append(placeCounts)
iterationTime.append(time.time() - iterationStartTime)
saveData = {
"objectClass": objectClass,
"nGraspSamples": nGraspSamples,
"graspScoreThresh": graspScoreThresh,
"nValueIterations": nValueIterations,
"nDataIterations": nDataIterations,
"nGraspIterations": nGraspIterations,
"pickEpsilon": pickEpsilon,
"placeEpsilon": placeEpsilon,
"minPickEpsilon": minPickEpsilon,
"minPlaceEpsilon": minPlaceEpsilon,
"pickEpsilonDelta": pickEpsilonDelta,
"placeEpsilonDelta": placeEpsilonDelta,
"maxExperiences": maxExperiences,
"trainingBatchSize": trainingBatchSize,
"averageReward": averageReward,
"placeActionCounts": placeActionCounts,
"trainLoss": trainLosses,
"testLoss": testLosses,
"databaseSize": databaseSize,
"iterationTime": iterationTime,
"placePoses": rlAgent.placePoses
}
savemat(saveFileName, saveData)
def main(objectClass, epsilon):
'''Entrypoint to the program.'''
# PARAMETERS =====================================================================================
# objects
nObjects = 7
randomObjectScale = True
targetObjectAxis = array([0, 0, 1])
maxAngleFromObjectAxis = 20 * (pi / 180)
maxObjectTableGap = 0.02
# view
viewCenter = array([0, 0, 0])
viewKeepout = 0.50
viewWorkspace = [(-1, 1), (-1, 1), (-1, 1)]
objViewWorkspace = [(-1, 1), (-1, 1), (0.002, 1)]
# grasps
graspDetectMode = 1 # 0=sample, 1=sample+label
nGraspSamples = 500
graspScoreThresh = 300
nGraspInliers = 2
# testing
weightsFileName = "/home/mgualti/mgualti/PickAndPlace/simulation/caffe/image_iter_5000.caffemodel"
nDataIterations = 300
# visualization/saving
saveFileName = "results-clutter-" + objectClass + "-epsilon" + str(
epsilon) + ".mat"
showViewer = False
showSteps = False
# INITIALIZATION =================================================================================
threeDNet = ThreeDNet()
rlEnv = RlEnvironment(showViewer)
rlAgent = RlAgent(rlEnv)
if epsilon < 1.0: rlAgent.LoadNetworkWeights(weightsFileName)
Return = []
# RUN TEST =======================================================================================
for dataIterationIdx in xrange(nDataIterations):
print("Iteration {}.".format(dataIterationIdx))
# place clutter on table
fullObjNames, objScales = threeDNet.GetRandomObjectSet(
objectClass, nObjects, randomObjectScale)
objHandles, objPoses = rlEnv.PlaceObjectSet(fullObjNames, objScales)
objCloud, objCloudIdxs = rlEnv.AssignPointsToObjects(
rlAgent, objHandles, viewCenter, viewKeepout, objViewWorkspace)
if showSteps:
raw_input("Objects placed.")
# move the hand to view position and capture a point cloud
cloud, viewPoints, viewPointIndices = rlAgent.GetDualCloud(
viewCenter, viewKeepout, viewWorkspace)
rlAgent.PlotCloud(cloud)
if showSteps:
raw_input("Point cloud.")
# detect grasps in the sensory data
graspsDetected = rlAgent.DetectGrasps(cloud, viewPoints,
viewPointIndices, nGraspSamples,
graspScoreThresh, nGraspInliers,
graspDetectMode)
grasps = rlAgent.FilterGraspsWithNoPoints(graspsDetected, objCloud)
if len(graspsDetected) > len(grasps):
print("Fitlered {} empty grasps.".format(
len(graspsDetected) - len(grasps)))
rlAgent.PlotGrasps(grasps)
if showSteps:
raw_input("Acquired grasps.")
if len(grasps) == 0:
print("No grasps found. Skipping iteration.")
rlEnv.RemoveObjectSet(objHandles)
rlAgent.UnplotGrasps()
rlAgent.UnplotCloud()
continue
# perform pick action
grasp = rlAgent.GetGrasp(grasps, epsilon)
rlAgent.PlotGrasps([grasp])
if showSteps:
raw_input("Selected grasp.")
# perform place action
P = rlAgent.GetPlacePose(grasp, epsilon)
rlAgent.MoveHandToPose(P)
objHandle, objPose = rlEnv.GetObjectWithMaxGraspPoints(
grasp, objHandles, objCloud, objCloudIdxs)
rlAgent.MoveObjectToHandAtGrasp(grasp, objHandle)
r = rlEnv.RewardHeightExponential(objHandle, targetObjectAxis,
maxAngleFromObjectAxis,
maxObjectTableGap)
print("The robot receives {} reward.".format(r))
Return.append(r)
if showSteps:
raw_input("Press [Enter] to continue...")
# cleanup this data iteration
rlEnv.RemoveObjectSet(objHandles)
rlAgent.UnplotGrasps()
rlAgent.UnplotCloud()
# Save results
saveData = {
"nObjects": nObjects,
"randomObjectScale": randomObjectScale,
"targetObjectAxis": targetObjectAxis,
"maxAngleFromObjectAxis": maxAngleFromObjectAxis,
"maxObjectTableGap": maxObjectTableGap,
"graspDetectMode": graspDetectMode,
"nGraspSamples": nGraspSamples,
"graspScoreThresh": graspScoreThresh,
"weightsFileName": weightsFileName,
"nDataIterations": nDataIterations,
"epsilon": epsilon,
"Return": Return
}
savemat(saveFileName, saveData)
