def main():
#load the environment
env_file_list = os.listdir(DATA_DIR+'/env_files/')
reward_matrix = []
for env_idx in range(1,len(env_file_list)):
train_env_f_name = get_file_name_with_given_idx(env_file_list,env_idx)
# load the environment file
print train_env_f_name
env=Environment()
env.Reset()
# env.SetViewer('qtcoin')
simple_prob=separate(env,0)
floor = env.GetKinBody("floorwalls")
floor.Enable(False)
oracle = ManipulationOracle(simple_prob,env)
robot = env.GetRobots()[0]
manipulator = robot.SetActiveManipulator('leftarm_torso')
## Recovering the environment
restore_env(env,train_env_f_name,DATA_DIR)
target = env.GetKinBody('ObjToG')
gmodel = databases.grasping.GraspingModel(robot,target)
if not gmodel.load():
gmodel.autogenerate()
ikmodel = databases.inversekinematics.InverseKinematicsModel(\
robot,iktype=IkParameterization.Type.Transform6D)
if not ikmodel.load():
ikmodel.autogenerate()
target.Enable(False)
grasps, grasp_indices = gmodel.computeValidGrasps(checkcollision=True,checkik=False)
thetas = sample_thetas(robot,oracle,gmodel,grasps,grasp_indices,env,3)
f_name = THETA_DIR + 'env_' + str(env_idx)+'.mat'
sio.savemat(f_name,{'thetas':thetas})
def main():
theta_values=[]
ik_times = []
plan_times = []
theta_augmented_with_grasp = []
env_file_list = os.listdir(DATA_DIR+'/env_files/')
reward_matrix = []
theta_file_template = 'env_'
if not os.path.isfile(SAVE_DIR+'thetas.mat'):
thetas = get_all_thetas(theta_file_template)
sio.savemat( SAVE_DIR + 'thetas.mat',{'thetas':thetas})
else:
thetas = sio.loadmat( SAVE_DIR + 'thetas.mat')['thetas']
# goal_config_list = get_goal_config_from_theta_list(thetas)
# sio.savemat( SAVE_DIR + 'goal_config_list.mat',{'goal_config_list':goal_config_list})
if len(sys.argv) == 1:
print "NEED ENV NUMBER"
return 0
env_idx = int(sys.argv[1])
train_env_f_name = get_file_name_with_given_idx(env_file_list,env_idx)
reward_file = SAVE_DIR + 'reward_matrix' +str(env_idx)+'.mat'
if os.path.isfile(reward_file):
print train_env_f_name +' already done'
return 0
print "SPAWNED: " + train_env_f_name
# load the environment file
# print train_env_f_name
env=Environment()
env.Reset()
simple_prob=separate(env,0)
floor = env.GetKinBody("floorwalls")
floor.Enable(False)
oracle = ManipulationOracle(simple_prob,env)
#env.SetViewer('qtcoin')
## Recovering the environment
restore_env(env,train_env_f_name,DATA_DIR)
# set the robot to default configuration
robot = env.GetRobots()[0]
manipulator = robot.SetActiveManipulator('leftarm_torso')
set_default_robot_config(robot) # set arms to the pregrasp pose
robot.SetActiveDOFs(manipulator.GetArmIndices())
evaluator = ThetaEvaluator(env,oracle)
env_theta_vals = []
env_plan_time = []
env_ik_time= []
tst = time.time()
for theta_idx in range(np.shape(thetas)[0]):
print "Running ENV " + str(theta_idx)
theta = thetas[theta_idx,:]
if len(theta) is not 0:
base_pose = theta[0:3]
grasp = theta[3:]
each = time.time()
theta_val,ik_time,plan_time = evaluator.get_true_vals_from_base_pose_and_grasp(grasp,base_pose)
else:
theta_val = 0
env_theta_vals.append(theta_val)
env_plan_time.append(plan_time)
env_ik_time.append(ik_time)
theta_values.append(env_theta_vals)
ik_times.append(env_ik_time)
plan_times.append(env_plan_time)
sio.savemat(SAVE_DIR + 'reward_matrix' +str(env_idx)+'.mat',\
{'reward_matrix':theta_values,'ik_times':ik_times,'plan_times':plan_times})
env.Destroy()
print "ENV " + str(env_idx) + "FINISHED RUNNING"
def main():
env_file_order = sio.loadmat(DATA_DIR+'env_file_order.mat')['file_orders'][0]
theta_values=[]
theta_augmented_with_grasp = []
for env_idx in range(160,len(env_file_order)):
# load the environment file
env_order = env_file_order[env_idx]
train_env_f_name = 'scene'+str(env_order)+'_env.dae'
print train_env_f_name
#theta_file_template = 'good_thetas_'
theta_file_template = 'good_thetas_leslie_'
thetas = sio.loadmat(THETA_DIR+theta_file_template+str(env_idx)+'.mat')['thetas']
if theta_file_template is not 'good_thetas_leslie_':
#artificial_vals= sio.loadmat(THETA_DIR+'good_thetas_'+\
# str(env_idx)+'.mat')['values']
#thetas = thetas[artificial_vals[0]>0,:]
thetas = get_all_thetas(theta_file_template)
else:
thetas = get_all_thetas(theta_file_template)
# sio.savemat('theta_values_leslie.mat',{'thetas':thetas})
env=Environment()
env.Reset()
simple_prob=separate(env,0)
floor = env.GetKinBody("floorwalls")
floor.Enable(False)
oracle = ManipulationOracle(simple_prob,env)
#env.SetViewer('qtcoin')
## Recovering the environment
restore_env(env,train_env_f_name,DATA_DIR)
# set the robot to default configuration
robot = env.GetRobots()[0]
manipulator = robot.SetActiveManipulator('leftarm_torso')
set_default_robot_config(robot) # set arms to the pregrasp pose
robot.SetActiveDOFs(manipulator.GetArmIndices())
evaluator = ThetaEvaluator(env,oracle)
env_theta_vals = []
for theta_idx in range(np.shape(thetas)[0]):
theta = thetas[theta_idx,:]
if theta_file_template == 'good_thetas_leslie_':
if len(theta) is not 0:
base_pose = theta[0:3]
grasp = theta[3:]
theta_val,ik_time,plan_time = evaluator.get_true_vals_from_base_pose_and_grasp(grasp,base_pose)
else:
theta_val = 0
else:
theta_val,g = evaluator.get_true_vals(theta)
if g is not None:
print g
theta_augmented_with_grasp.append( np.r_[theta,g] )
else:
theta_augmented_with_grasp.append( np.r_[theta] )
env_theta_vals.append(theta_val)
theta_values.append(env_theta_vals)
if theta_file_template == 'good_thetas_leslie_':
sio.savemat('theta_rewards_leslile5.mat',{'reward':theta_values})
def main():
theta_values=[]
theta_augmented_with_grasp = []
env_file_list = os.listdir(DATA_DIR+'/env_files/')
reward_matrix = []
theta_file_template = 'env_'
thetas = get_all_thetas(theta_file_template)
sio.savemat( SAVE_DIR + 'thetas.mat',{'thetas':thetas})
# goal_config_list = get_goal_config_from_theta_list(thetas)
# sio.savemat( SAVE_DIR + 'goal_config_list.mat',{'goal_config_list':goal_config_list})
for env_idx in range(len(env_file_list)):
train_env_f_name = get_file_name_with_given_idx(env_file_list,env_idx)
# load the environment file
print train_env_f_name
env=Environment()
env.Reset()
simple_prob=separate(env,0)
floor = env.GetKinBody("floorwalls")
floor.Enable(False)
oracle = ManipulationOracle(simple_prob,env)
#env.SetViewer('qtcoin')
## Recovering the environment
restore_env(env,train_env_f_name)
# set the robot to default configuration
robot = env.GetRobots()[0]
manipulator = robot.SetActiveManipulator('leftarm_torso')
set_default_robot_config(robot) # set arms to the pregrasp pose
robot.SetActiveDOFs(manipulator.GetArmIndices())
evaluator = ThetaEvaluator(env,oracle)
env_theta_vals = []
env_plan_time = []
env_ik_time= []
tst = time.time()
for theta_idx in range(np.shape(thetas)[0]):
theta = thetas[theta_idx,:]
if len(theta) is not 0:
base_pose = theta[0:3]
grasp = theta[3:]
each = time.time()
theta_val,ik_time,plan_time = evaluator.get_true_vals_from_base_pose_and_grasp(grasp,base_pose)
else:
theta_val = 0
env_theta_vals.append(theta_val)
env_plan_time.append(plan_time)
env_ik_time.append(ik_time)
print time.time() - tst
theta_values.append(env_theta_vals)
ik_times.append(env_ik_time)
plan_times.append(env_plan_time)
sio.savemat(SAVE_DIR + 'reward_matrix.mat',{'reward_matrix':theta_values,'ik_times':ik_times,'plan_times':plan_times})
env.Destroy()
