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from numpy import ones, zeros, random
from sklearn.metrics import mean_squared_error as MSE
random.seed(123)
OBJECTS = {
"book": {"dims": [4, 4, 2], "stack": True},
"penstand": {"dims": [2, 2, 4], "stack": True},
"bottle": {"dims": [2, 2, 6], "stack": False},
"pen": {"dims": [1, 1, 4], "stack": False},
"pencil": {"dims": [1, 1, 6], "stack": False},
"eraser": {"dims": [2, 1, 1], "stack": False},
"powerbank": {"dims": [4, 2, 1], "stack": False},
"mobile": {"dims": [4, 2, 1], "stack": False},
"laptop": {"dims": [6, 4, 1], "stack": True},
"monitor": {"dims": [6, 4, 2], "stack": False},
"keyboard": {"dims": [6, 2, 1], "stack": False},
"mouse": {"dims": [4, 2, 1], "stack": False},
"headphones": {"dims": [4, 4, 2], "stack": False},
"charger": {"dims": [2, 2, 1], "stack": False},
"notebook": {"dims": [4, 4, 1], "stack": True},
"folder": {"dims": [4, 4, 1], "stack": True},
"backpack": {"dims": [6, 4, 2], "stack": False},
"pouch": {"dims": [4, 4, 2], "stack": False},
}
OBJECT_NAMES = [
"book",
"penstand",
"bottle",
"pen",
"pencil",
"eraser",
"powerbank",
"mobile",
"laptop",
"monitor",
"keyboard",
"mouse",
"headphones",
"charger",
"notebook",
"folder",
"backpack",
"pouch",
]
ACTION_CONFIG = {
"RIGHT": [1, 0, 0],
"LEFT": [-1, 0, 0],
"UP": [0, 1, 0],
"DOWN": [0, -1, 0],
"FORWARD": [0, 0, 1],
"BACKWARD": [0, 0, -1],
"ROTATE": [0, 0, 0],
}
def appendRewardFeedback(state, choice, feedback, reward):
state.rewardList.append(reward)
state.rewardFeedback.append(feedback)
if choice == "segment":
state.rewardFeedbackSegment.append(feedback)
state.rewardListSegment.append(reward)
elif choice == "place":
state.rewardFeedbackPlace.append(feedback)
state.rewardListPlace.append(reward)
elif choice == "adjust":
state.rewardFeedbackAdjust.append(feedback)
state.rewardListAdjust.append(reward)
def initDimentions(obj):
dims = obj.get("dims")
if dims is None:
return []
return ones(dims, dtype=int).tolist()
def initGrid():
sizeX, sizeY, sizeZ = randint(8, 12), randint(8, 12), randint(8, 12)
grid = zeros((sizeX, sizeY, sizeZ), dtype=int).tolist()
numObjs = randint(3, len(OBJECT_NAMES) + 1)
chosenNames = random.choice(OBJECT_NAMES, size=numObjs, replace=False)
placed = {}
for name in chosenNames:
obj = OBJECTS.get(name)
dimX, dimY, dimZ = obj["dims"]
if dimX > sizeX or dimY > sizeY or dimZ > sizeZ:
continue
isPlaced = False
tryPlaced = 0
while not isPlaced and tryPlaced < 100:
posX = randint(0, sizeX - dimX + 1)
posY = randint(0, sizeY - dimY + 1)
posZ = 0
canPlace = True
for i in range(dimX):
for j in range(dimY):
for k in range(dimZ):
if (
grid[posX + i][posY + j][posZ + k] != 0
and obj["stack"] == False
):
canPlace = False
break
else:
canPlace = True
if not canPlace:
break
if not canPlace:
break
if canPlace:
for i in range(dimX):
for j in range(dimY):
for k in range(dimZ):
if (
obj["stack"]
and grid[posX + i][posY + j][posZ + k] > 0
and posZ + k + 1 < sizeZ
):
grid[posX + i][posY + j][posZ + k + 1] += 1
else:
grid[posX + i][posY + j][posZ + k] += 1
placed[name] = (posX, posY, posZ, obj["stack"])
isPlaced = True
return (grid, placed)
def initWeightedGrid(shape=None):
if shape is None:
shape = (randint(8, 12), randint(8, 12), randint(8, 12))
grid = random.uniform(0, 1, shape)
x_mid = grid.shape[0] // 2
x_span = grid.shape[0] // 4
y_front = grid.shape[1] // 3
grid[x_mid - x_span : x_mid + x_span, :y_front, :] *= 0.2
return grid
def _get_weight_value(weight, x, y, z):
if not weight or not weight[0] or not weight[0][0]:
return 0.0
if (
x < 0
or y < 0
or z < 0
or x >= len(weight)
or y >= len(weight[0])
or z >= len(weight[0][0])
):
return 0.0
return weight[x][y][z]
def place(segment, objects, state):
dims = state.currentGrid
weight = state.weightedGrid
objsPresent = state.ObjectsPresent
reward = 0.0
totalObjs = len(objects)
reward_per_obj_placed = 45.0 / totalObjs
placement_failed = False
if segment:
appendRewardFeedback(
state, "place", "Placing objects with segmentation is not allowed.", -60.0
)
return (-60.0, True)
for obj_name, pos in objects.items():
obj = OBJECTS.get(obj_name)
if obj is None:
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' is not recognized.",
-reward_per_obj_placed,
)
reward -= reward_per_obj_placed
continue
objGrid = initDimentions(obj)
for i in range(len(objGrid)):
for j in range(len(objGrid[0])):
for k in range(len(objGrid[0][0])):
if (
pos[0] + i >= len(dims)
or pos[1] + j >= len(dims[0])
or pos[2] + k >= len(dims[0][0])
):
reward -= reward_per_obj_placed
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' placement is out of bounds.",
-reward_per_obj_placed,
)
placement_failed = True
break
if dims[pos[0] + i][pos[1] + j][pos[2] + k] > 0 and pos[3] == False:
reward -= reward_per_obj_placed
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' placement overlaps with another object and stacking is not allowed.",
-reward_per_obj_placed,
)
placement_failed = True
break
elif (
dims[pos[0] + i][pos[1] + j][pos[2] + k] > 0 and pos[3] == True
):
if pos[2] + k + 1 < len(dims[0][0]):
dims[pos[0] + i][pos[1] + j][pos[2] + k + 1] += 1
bonus = (
_get_weight_value(
weight,
pos[0] + i,
pos[1] + j,
pos[2] + k + 1,
)
* reward_per_obj_placed
)
reward += bonus
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' placed with stacking. Bonus: {bonus:.2f}",
bonus,
)
else:
reward -= reward_per_obj_placed
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' placement failed. No space for stacking.",
-reward_per_obj_placed,
)
placement_failed = True
break
else:
dims[pos[0] + i][pos[1] + j][pos[2] + k] = 1
bonus = reward_per_obj_placed * _get_weight_value(
weight, pos[0] + i, pos[1] + j, pos[2] + k
)
reward += bonus
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' placed successfully. Bonus: {bonus:.2f}",
bonus,
)
if placement_failed:
break
if placement_failed:
break
if not placement_failed:
state.ObjectsPlaced[obj_name] = pos
state.numberPlaced += 1
try:
if objsPresent[obj_name] == state.ObjectsPlaced[obj_name]:
reward -= 45.0 / totalObjs
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' is being placed in the same location",
-reward_per_obj_placed,
)
except KeyError:
reward -= reward_per_obj_placed
appendRewardFeedback(
state,
"place",
f"Object '{obj_name}' is present in the environment, but is placed in same location as originally found.",
-reward_per_obj_placed,
)
continue
return (reward, placement_failed)
def findobject(segment, objects, state):
if not segment or segment is None:
appendRewardFeedback(
state,
"segment",
"Finding objects without segmentation is not allowed.",
-60.0,
)
return -60.0
if set(state.objectsFound) == set(state.ObjectsPresent.keys()):
appendRewardFeedback(
state,
"segment",
"No point in finding more objects as all are already found. Make the isSegement attribute false and execute the place method.",
-60.0,
)
return -60.0
reward = 0.0
glMetric = 45.0 / len(state.ObjectsPresent)
objs = []
for obj_found, pos_found in objects.items():
pos_real = state.ObjectsPresent.get(obj_found)
if pos_real is None:
reward -= glMetric
appendRewardFeedback(
state,
"segment",
f"Object '{obj_found}' not found in the environment.",
-glMetric,
)
continue
if pos_found == pos_real:
reward += glMetric
appendRewardFeedback(
state,
"segment",
f"Object '{obj_found}' found with correct position and stacking.",
glMetric,
)
objs.append(obj_found)
else:
mse = MSE(pos_real[:3], pos_found[:3])
reward -= mse
appendRewardFeedback(
state,
"segment",
f"Object '{obj_found}' found with incorrect position. MSE: {mse:.2f}",
-mse,
)
if pos_found[3] != pos_real[3]:
reward -= glMetric / 4.0
appendRewardFeedback(
state,
"segment",
f"Object '{obj_found}' found with incorrect stacking. Penalty: {glMetric / 4.0}",
-glMetric / 4.0,
)
else:
reward += glMetric / 4.0
appendRewardFeedback(
state,
"segment",
f"Object '{obj_found}' found with correct stacking. Bonus: {glMetric / 4.0}",
glMetric / 4.0,
)
for obj in objs:
if obj in state.objectsLeft:
state.objectsLeft.remove(obj)
if obj not in state.objectsFound:
state.objectsFound.append(obj)
return reward
def _remove_object(state, obj_name):
reward = 0
try:
pos = state.ObjectsPlaced.pop(obj_name)
except KeyError:
reward -= 45.0 / len(state.ObjectsPresent)
appendRewardFeedback(
state,
"adjust",
f"Object '{obj_name}' is not placed in the environment.",
-reward,
)
return reward
state.numberPlaced -= 1
dims = state.currentGrid
obj = OBJECTS.get(obj_name)
objGrid = initDimentions(obj)
for i in range(len(objGrid)):
for j in range(len(objGrid[0])):
for k in range(len(objGrid[0][0])):
if dims[pos[0] + i][pos[1] + j][pos[2] + k] > 0:
dims[pos[0] + i][pos[1] + j][pos[2] + k] -= 1
def _adjustment_helper(state, name, pos, change, direction):
_remove_object(state, name)
if direction == "ROTATE":
newPos = (pos[1], pos[0], pos[2], pos[3])
else:
newPos = (pos[0] + change[0], pos[1] + change[1], pos[2] + change[2], pos[3])
reward, isNotPlaced = place(False, {name: newPos}, state)
if isNotPlaced:
dummyReward = place(False, {name: pos}, state)[0]
appendRewardFeedback(
state,
"adjust",
f"Failed to adjust object '{name}' in direction {direction}. Reverting to original position.",
-dummyReward,
)
return -dummyReward
appendRewardFeedback(
state,
"adjust",
f"Object '{name}' moved {direction} successfully.",
reward,
)
return reward
def adjustment(segment, action, state):
objsPlaced = state.ObjectsPlaced
if segment:
appendRewardFeedback(
state, "adjust", "Placing objects with segmentation is not allowed.", -60.0
)
return -60.0
try:
initPos = objsPlaced[action[0]]
name = action[0]
except KeyError:
reward_per_obj_placed = 45.0 / len(state.ObjectsPresent)
appendRewardFeedback(
state,
"adjust",
f"Object '{action[0]}' is not placed in the environment, so it cannot be adjusted.",
-reward_per_obj_placed,
)
return -reward_per_obj_placed
if action[1] in ACTION_CONFIG:
reward = _adjustment_helper(
state, name, initPos, ACTION_CONFIG.get(action[1]), action[1]
)
return reward
else:
reward_per_obj_placed = 45.0 / len(state.ObjectsPresent)
appendRewardFeedback(
state,
"adjust",
f"Invalid adjustment direction '{action[1]}'. Valid directions are RIGHT, LEFT, UP, DOWN, FORWARD, BACKWARD, ROTATE.",
-reward_per_obj_placed,
)
return -reward_per_obj_placed
|