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():
'''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), (-1, 1)]
# grasps
graspDetectMode = 1 # 0=sample, 1=sample+label
nGraspSamples = 200
graspScoreThresh = 300
# learning
nTrainingIterations = 100
nEpisodes = 100
nReuses = 10
maxTimesteps = 10
gamma = 0.98
epsilon = 1.0
epsilonDelta = 0.05
minEpsilon = 0.05
maxExperiences = 50000
trainingBatchSize = 50000
unbiasOnIteration = nTrainingIterations - 5
# visualization/saving
saveFileName = "results.mat"
recordLoss = True
showViewer = False
showSteps = False
# INITIALIZATION =================================================================================
threeDNet = ThreeDNet()
rlEnv = RlEnvironment(showViewer)
rlAgent = RlAgent(rlEnv)
nPlaceOptions = len(rlAgent.placePoses)
experienceDatabase = []
# RUN TEST =======================================================================================
avgReturn = []
avgGraspsDetected = []
avgTopGraspsDetected = []
placeHistograms = []
avgGoodTempPlaceCount = []
avgBadTempPlaceCount = []
avgGoodFinalPlaceCount = []
avgBadFinalPlaceCount = []
trainLosses = []
testLosses = []
databaseSize = []
iterationTime = []
for trainingIteration in xrange(nTrainingIterations):
# initialization
iterationStartTime = time.time()
print("Iteration: {}, Epsilon: {}".format(trainingIteration, epsilon))
placeHistogram = zeros(nPlaceOptions)
Return = []
graspsDetected = []
topGraspsDetected = []
goodTempPlaceCount = []
badTempPlaceCount = []
goodFinalPlaceCount = []
badFinalPlaceCount = []
# check if it's time to unbias data
if trainingIteration >= unbiasOnIteration:
maxExperiences = trainingBatchSize # selects all recent experiences, unbiased
epsilon = 0 # estimating value function of actual policy
# for each episode/object placement
for episode in xrange(nEpisodes):
# 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(s) and capture a point cloud
cloud, viewPoints, viewPointIndices = rlAgent.GetDualCloud(
viewCenter, viewKeepout, viewWorkspace)
# detect grasps in the sensor data
grasps = rlAgent.DetectGrasps(cloud, viewPoints, viewPointIndices,
nGraspSamples, graspScoreThresh,
graspDetectMode)
graspsStart = grasps
graspsDetected.append(len(grasps))
topGraspsCount = CountObjectTopGrasps(grasps, objRandPose,
maxAngleFromObjectAxis)
if len(grasps) == 0:
print("No grasps found. Skipping iteration.")
rlEnv.RemoveObject(objHandle)
continue
rlAgent.PlotCloud(cloud)
rlAgent.PlotGrasps(grasps)
for reuse in xrange(nReuses):
print("Episode {}.{}.{}.".format(trainingIteration, episode,
reuse))
if showSteps:
raw_input(
"Beginning of episode. Press [Enter] to continue...")
# initialize recording variables
episodePlaceHistogram = zeros(nPlaceOptions)
episodeReturn = 0
episodeGoodTempPlaceCount = 0
episodeBadTempPlaceCount = 0
episodeGoodFinalPlaceCount = 0
episodeBadFinalPlaceCount = 0
graspDetectionFailure = False
episodeExperiences = []
# initial state and first action
s, selectedGrasp = rlEnv.GetInitialState(rlAgent)
a, grasp, place = rlAgent.ChooseAction(s, grasps, epsilon)
rlAgent.PlotGrasps([grasp])
# for each time step in the episode
for t in xrange(maxTimesteps):
ss, selectedGrasp, rr = rlEnv.Transition(
rlAgent, objHandle, s, selectedGrasp, a, grasp, place,
targetObjectAxis, maxAngleFromObjectAxis,
maxObjectTableGap)
ssIsPlacedTempGood = ss[1][1]
ssIsPlacedTempBad = ss[1][2]
ssIsPlacedFinalGood = ss[1][3]
ssIsPlacedFinalBad = ss[1][4]
if showSteps:
raw_input(
"Transition {}. Press [Enter] to continue...".
format(t))
# re-detect only if a non-terminal placement just happened
if ssIsPlacedTempGood and place is not None:
cloud, viewPoints, viewPointIndices = rlAgent.GetDualCloud(
viewCenter, viewKeepout, viewWorkspace)
grasps = rlAgent.DetectGrasps(cloud, viewPoints,
viewPointIndices,
nGraspSamples,
graspScoreThresh,
graspDetectMode)
graspsDetected.append(len(grasps))
topGraspsCount = CountObjectTopGrasps(
grasps, rlEnv.GetObjectPose(objHandle),
maxAngleFromObjectAxis)
topGraspsDetected.append(topGraspsCount)
if len(grasps) == 0:
print("Grasp detection failure.")
graspDetectionFailure = True
break
rlAgent.PlotCloud(cloud)
rlAgent.PlotGrasps(grasps)
# get next action
aa, ggrasp, pplace = rlAgent.ChooseAction(
ss, grasps, epsilon)
if ggrasp is not None: rlAgent.PlotGrasps([ggrasp])
if showSteps:
raw_input(
"Action {}. Press [Enter] to continue...".format(
t))
# add to database and record data
episodeExperiences.append((s, a, rr, ss, aa))
episodeReturn += (gamma**t) * rr
if place is not None:
episodeGoodTempPlaceCount += ssIsPlacedTempGood
episodeBadTempPlaceCount += ssIsPlacedTempBad
episodeGoodFinalPlaceCount += ssIsPlacedFinalGood
episodeBadFinalPlaceCount += ssIsPlacedFinalBad
placeHistogram += a[1]
# prepare for next time step
if ssIsPlacedTempBad or ssIsPlacedFinalGood or ssIsPlacedFinalBad:
break
s = ss
a = aa
grasp = ggrasp
place = pplace
# cleanup this reuse
if not graspDetectionFailure:
experienceDatabase += episodeExperiences
placeHistogram += episodePlaceHistogram
Return.append(episodeReturn)
goodTempPlaceCount.append(episodeGoodTempPlaceCount)
badTempPlaceCount.append(episodeBadTempPlaceCount)
goodFinalPlaceCount.append(episodeGoodFinalPlaceCount)
badFinalPlaceCount.append(episodeBadFinalPlaceCount)
rlEnv.MoveObjectToPose(objHandle, objRandPose)
grasps = graspsStart
# cleanup this episode
rlEnv.RemoveObject(objHandle)
rlAgent.UnplotGrasps()
rlAgent.UnplotCloud()
# 2. Compute value labels for data.
experienceDatabase = rlAgent.PruneDatabase(experienceDatabase,
maxExperiences)
Dl = rlAgent.DownsampleAndLabelData(experienceDatabase,
trainingBatchSize, gamma)
databaseSize.append(len(experienceDatabase))
# 3. Train network from replay database.
trainLoss, testLoss = rlAgent.Train(Dl, recordLoss=recordLoss)
trainLosses.append(trainLoss)
testLosses.append(testLoss)
epsilon -= epsilonDelta
epsilon = max(minEpsilon, epsilon)
# 4. Save results
avgReturn.append(mean(Return))
avgGraspsDetected.append(mean(graspsDetected))
avgTopGraspsDetected.append(mean(topGraspsDetected))
placeHistograms.append(placeHistogram)
avgGoodTempPlaceCount.append(mean(goodTempPlaceCount))
avgBadTempPlaceCount.append(mean(badTempPlaceCount))
avgGoodFinalPlaceCount.append(mean(goodFinalPlaceCount))
avgBadFinalPlaceCount.append(mean(badFinalPlaceCount))
iterationTime.append(time.time() - iterationStartTime)
saveData = {
"objectClass": objectClass,
"randomScale": randomScale,
"maxAngleFromObjectAxis": maxAngleFromObjectAxis,
"maxObjectTableGap": maxObjectTableGap,
"nGraspSamples": nGraspSamples,
"graspScoreThresh": graspScoreThresh,
"graspDetectMode": graspDetectMode,
"nTrainingIterations": nTrainingIterations,
"nEpisodes": nEpisodes,
"maxTimesteps": maxTimesteps,
"gamma": gamma,
"epsilon": epsilon,
"minEpsilon": minEpsilon,
"epsilonDelta": epsilonDelta,
"maxExperiences": maxExperiences,
"trainingBatchSize": trainingBatchSize,
"avgReturn": avgReturn,
"avgGraspsDetected": avgGraspsDetected,
"avgTopGraspsDetected": avgTopGraspsDetected,
"placeHistograms": placeHistograms,
"avgGoodTempPlaceCount": avgGoodTempPlaceCount,
"avgBadTempPlaceCount": avgBadTempPlaceCount,
"avgGoodFinalPlaceCount": avgGoodFinalPlaceCount,
"avgBadFinalPlaceCount": avgBadFinalPlaceCount,
"trainLoss": trainLosses,
"testLoss": testLosses,
"databaseSize": databaseSize,
"iterationTime": iterationTime,
"placePoses": rlAgent.placePoses
}
savemat(saveFileName, saveData)
