=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task where a robot picks up three items from a table and places them on a plate. It begins by setting up the environment and resetting the task to its initial state. Video recording is optionally initialized to capture the simulation. The positions of the items and the plate are retrieved. 

The robot then sequentially performs the following steps: it picks up the first item from the table and places it on the plate, followed by picking the second item and placing it on the plate, and finally picking the third item and placing it on the plate. After each action, the code checks if the task is completed and prints appropriate messages. 

At the end of the task, the environment is shut down properly. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK describes a specific instruction for a robot to follow, while CODE_FUNC outlines a function that implements a simulation of that instruction.
2. **Level of Abstraction**: TASK is a high-level directive, whereas CODE_FUNC provides a detailed procedural implementation of that directive.
3. **Context**: TASK is presented in a planning domain definition language (PDDL) context, while CODE_FUNC is described in the context of a programming function.
4. **Execution**: TASK does not specify how the actions are executed, while CODE_FUNC details the sequential execution of actions and checks for task completion.
5. **Initialization**: TASK assumes an initial state defined in PDDL, while CODE_FUNC includes steps for setting up the environment and resetting the task.
6. **Output**: TASK does not mention any output or feedback, whereas CODE_FUNC includes print statements to indicate task progress and completion.
7. **Video Recording**: CODE_FUNC mentions optional video recording of the simulation, which is not present in TASK.
8. **Sequential Actions**: TASK implies a sequence of actions but does not explicitly state the order, while CODE_FUNC clearly outlines the order of picking and placing items.
9. **Shutdown Procedure**: CODE_FUNC includes a shutdown procedure for the environment, which is not addressed in TASK. 

Overall, the two serve different purposes and levels of detail in the context of robotic task execution.

Refined Code Generated:
-------------------
import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment
from skill_code import pick, place
from video import init_video_writers, recording_step, recording_get_observation
from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        _, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define object positions
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        item3_pos = positions['item3']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        for item_pos in [item1_pos, item2_pos, item3_pos]:
            item_name = f"item{[item1_pos, item2_pos, item3_pos].index(item_pos) + 1}"
            print(f"[Task] Picking {item_name} from the table.")
            obs, reward, done = pick(
                env,
                task,
                target_pos=item_pos,
                approach_distance=0.15,
                max_steps=100,
                threshold=0.01,
                approach_axis='z',
                timeout=10.0
            )
            if done:
                print(f"[Task] Task ended after picking {item_name}!")
                return

            print(f"[Task] Placing {item_name} on the plate.")
            obs, reward, done = place(
                env,
                task,
                target_pos=plate_pos,
                approach_distance=0.15,
                max_steps=100,
                threshold=0.01,
                approach_axis='z',
                timeout=10.0
            )
            if done:
                print(f"[Task] Task ended after placing {item_name}!")
                return

        print("[Task] Task completed successfully!")

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")
-------------------

=== Instruction 2 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing objects. It begins by setting up the environment and resetting the task to its initial state. It optionally initializes video recording for the simulation. The function retrieves the positions of two items and a plate. 

The task consists of four main steps: first, it picks the first item from the table and places it onto the plate; second, it picks the second item from the table and places it onto the plate as well. After each action, it checks if the task is completed. If the task ends at any point, it prints a message and exits. Finally, it ensures that the environment is properly shut down before concluding the task.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robot to perform (dropping tomatoes onto a plate).
   - CODE_FUNC outlines a function that simulates a series of actions (picking and placing items) in a broader context.

2. **Level of Detail**:
   - TASK provides a single, clear instruction without elaboration on the process.
   - CODE_FUNC details the steps involved in executing the task, including setup, execution, and shutdown procedures.

3. **Context**:
   - TASK is presented in a PDDL (Planning Domain Definition Language) format, which is used for defining planning problems.
   - CODE_FUNC is described in a programming context, indicating it is part of a simulation function.

4. **Action Sequence**:
   - TASK specifies a single action (dropping tomatoes).
   - CODE_FUNC describes a sequence of actions (picking and placing two items), indicating a more complex operation.

5. **Completion Check**:
   - TASK does not mention any checks for task completion.
   - CODE_FUNC includes a mechanism to check if the task is completed after each action.

6. **Environment Management**:
   - TASK does not address the management of the environment or state.
   - CODE_FUNC includes steps for initializing and shutting down the environment.

7. **Output**:
   - TASK does not specify any output or feedback mechanism.
   - CODE_FUNC mentions printing a message upon task completion.

8. **Initialization**:
   - TASK assumes an initial state without detailing how it is set up.
   - CODE_FUNC explicitly mentions resetting the task to its initial state.

9. **Recording**:
   - TASK does not mention any form of recording or monitoring.
   - CODE_FUNC includes an optional video recording feature for the simulation.

10. **Object Specification**:
    - TASK refers to "2 tomatoes" generically.
    - CODE_FUNC refers to "two items" and a "plate," indicating a more abstract representation of objects.

11. **Action Specificity**:
    - TASK is specific to dropping tomatoes.
    - CODE_FUNC is more general, involving picking and placing actions that can apply to various objects.

12. **Error Handling**:
    - TASK does not include any error handling or contingencies.
    - CODE_FUNC implies a structured approach to handling task completion and potential exits.

In summary, the semantic differences between TASK and CODE_FUNC are significant, reflecting different levels of detail, context, and operational complexity.

Refined Code Generated:
-------------------
import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment

from skill_code import pick, place
from video import init_video_writers, recording_step, recording_get_observation
from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        descriptions, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define object positions
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        plate_pos = positions['plate']

        # === Task Plan Execution ===
        # Step 1: Pick item1 from the table
        print("[Task] Picking item1 from the table.")
        obs, reward, done = pick(env, task, target_pos=item1_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item1!")
            return

        # Step 2: Place item1 onto the plate
        print("[Task] Placing item1 onto the plate.")
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after placing item1!")
            return

        # Step 3: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, reward, done = pick(env, task, target_pos=item2_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 4: Place item2 onto the plate
        print("[Task] Placing item2 onto the plate.")
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")

if __name__ == "__main__":
    run_skeleton_task()
-------------------

=== Instruction 3 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing items. It begins by setting up the environment and resetting the task to its initial state. Video recording is optionally initialized to capture the simulation. The positions of three items and a plate are retrieved. 

The task consists of a series of steps where the program picks each item from a table and places it on the plate. After each pick and place action, the program checks if the task is completed. If the task ends at any step, it prints a message and exits. Finally, the environment is shut down properly after the task execution. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robot to perform (picking up tomatoes and placing them on a plate).
   - CODE_FUNC outlines a function that implements a simulation of the task, including setup, execution, and cleanup.

2. **Level of Abstraction**: 
   - TASK is a high-level description of an action to be performed.
   - CODE_FUNC provides a detailed procedural implementation of that action within a simulation environment.

3. **Components**: 
   - TASK includes domain-specific language (PDDL) for defining actions, predicates, and problem states.
   - CODE_FUNC includes programming constructs (functions, loops, conditionals) to manage the execution of the task.

4. **Execution Flow**: 
   - TASK does not specify how the actions are executed or in what order.
   - CODE_FUNC explicitly describes the sequence of actions (picking and placing items) and checks for task completion.

5. **Environment Interaction**: 
   - TASK defines the initial state and conditions for the robot's actions.
   - CODE_FUNC includes environment setup, state resetting, and proper shutdown after task execution.

6. **Output Handling**: 
   - TASK does not mention any output or feedback mechanism.
   - CODE_FUNC includes print statements to indicate task completion and handles the simulation's output.

7. **Simulation Aspect**: 
   - TASK is a theoretical instruction without any simulation context.
   - CODE_FUNC is designed to run within a simulation framework, capturing the dynamics of the task execution.

8. **Error Handling**: 
   - TASK does not address error handling or task failure.
   - CODE_FUNC includes checks for task completion and exits if the task ends prematurely.

9. **Recording**: 
   - TASK does not mention any form of recording or monitoring.
   - CODE_FUNC optionally initializes video recording to capture the simulation.

10. **Execution Context**: 
    - TASK is a standalone instruction applicable in various contexts.
    - CODE_FUNC is tied to a specific programming environment and is executed as part of a script. 

In summary, the semantic differences between TASK and CODE_FUNC are significant, as TASK is a high-level directive while CODE_FUNC is a detailed implementation of that directive within a simulation framework.

Refined Code Generated:
-------------------
import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment
from skill_code import pick, place
from video import init_video_writers, recording_step, recording_get_observation
from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        _, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define object positions
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        item3_pos = positions['item3']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        for item_pos in [item1_pos, item2_pos, item3_pos]:
            item_name = f"item{[item1_pos, item2_pos, item3_pos].index(item_pos) + 1}"
            print(f"[Task] Picking {item_name} from the table.")
            obs, reward, done = pick(env, task, target_pos=item_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
            if done:
                print(f"[Task] Task ended after picking {item_name}!")
                return

            print(f"[Task] Placing {item_name} on the plate.")
            obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
            if done:
                print(f"[Task] Task ended after placing {item_name}!")
                return

        print("[Task] Task completed successfully! Reward:", reward)

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")

if __name__ == "__main__":
    run_skeleton_task()
-------------------

=== Instruction 4 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing objects. It begins by setting up the environment and resetting the task to its initial state. It optionally initializes video recording for the simulation. The function retrieves the positions of two items and a plate. 

The task consists of four main steps: first, it picks the first item from the table and checks if the task is complete; next, it places that item onto the plate and again checks for task completion; then, it picks the second item from the table and checks for completion; finally, it places the second item onto the plate and checks if the task is successfully completed. 

Throughout the process, it prints status messages to indicate the progress of the task. Regardless of the outcome, it ensures that the environment is properly shut down at the end. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK defines a specific problem in a planning domain for a robot to execute, while CODE_FUNC describes a function that simulates the execution of that task programmatically.

2. **Structure**: 
   - TASK is structured in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems and domains. CODE_FUNC is written in a programming language (likely Python) and describes procedural steps.

3. **Execution**: 
   - TASK outlines the preconditions and effects of actions in a formal way, focusing on what needs to be true before and after actions. CODE_FUNC describes a sequence of operations to be performed in a simulation, focusing on the implementation of those actions.

4. **Detail Level**: 
   - TASK provides a high-level description of the actions (pick and place) and their conditions, while CODE_FUNC goes into detail about the steps taken during the simulation, including status messages and environment management.

5. **Output**: 
   - TASK does not specify any output or feedback mechanism; it is purely a definition of actions and states. CODE_FUNC includes status messages to indicate progress and completion of the task.

6. **Environment Management**: 
   - TASK does not address how the environment is set up or reset, while CODE_FUNC explicitly mentions resetting the environment and shutting it down after execution.

7. **Task Completion Check**: 
   - TASK does not include any mechanism for checking if the task is complete; it simply defines actions. CODE_FUNC includes checks for task completion after each action.

8. **Initialization**: 
   - TASK assumes an initial state defined in the problem, while CODE_FUNC mentions initializing the environment and optionally starting video recording.

9. **Execution Context**: 
   - TASK is a standalone definition that can be used in various planning systems, while CODE_FUNC is contextually tied to a specific programming environment and execution context (e.g., running as a main program).

10. **Flexibility**: 
    - TASK is more abstract and can be adapted to different scenarios within the same domain, while CODE_FUNC is likely tailored to a specific implementation of the task described in TASK. 

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and are structured differently.

Refined Code Generated:
-------------------
import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment

from skill_code import pick, place
from video import init_video_writers, recording_step, recording_get_observation
from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        descriptions, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define object positions
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        plate_pos = positions['plate']

        # === Execute Task Plan ===
        # Step 1: Pick item1 from the table
        print("[Task] Picking item1 from the table.")
        obs, reward, done = pick(env, task, target_pos=item1_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item1!")
            return

        # Step 2: Place item1 onto the plate
        print("[Task] Placing item1 onto the plate.")
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after placing item1!")
            return

        # Step 3: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, reward, done = pick(env, task, target_pos=item2_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 4: Place item2 onto the plate
        print("[Task] Placing item2 onto the plate.")
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")

if __name__ == "__main__":
    run_skeleton_task()
-------------------

=== Instruction 5 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing items. It begins by setting up the environment and resetting the task to its initial state. It optionally initializes video recording for the simulation. The function retrieves the positions of two items and a plate. 

The task consists of four main steps: first, it picks the first item from the table and places it onto the plate; next, it picks the second item from the table and places it onto the plate as well. After each action, it checks if the task is completed and prints appropriate messages. Finally, it ensures that the environment is properly shut down after the task execution. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robot to perform (tossing tomatoes), while CODE_FUNC outlines a function that simulates the process of picking and placing items.

2. **Level of Abstraction**: 
   - TASK is a high-level instruction without implementation details, whereas CODE_FUNC provides a detailed procedural description of how to execute the task.

3. **Implementation Details**: 
   - TASK does not specify how the tomatoes are tossed, while CODE_FUNC includes steps for picking and placing items, including checking task completion and managing the environment.

4. **Context**: 
   - TASK is presented in the context of a planning domain (PDDL), focusing on the actions and their effects, while CODE_FUNC is in the context of a programming function that executes a simulation.

5. **Action Sequence**: 
   - TASK implies a single action of tossing two tomatoes, while CODE_FUNC explicitly breaks down the task into multiple steps (picking and placing each item).

6. **Output and Feedback**: 
   - TASK does not mention any output or feedback mechanism, while CODE_FUNC includes checks for task completion and prints messages.

7. **Environment Management**: 
   - TASK does not address the management of the environment, while CODE_FUNC includes steps to reset and shut down the environment after task execution.

8. **Recording**: 
   - TASK does not mention video recording, whereas CODE_FUNC includes an optional step for initializing video recording during the simulation.

9. **Execution Context**: 
   - TASK is a standalone instruction, while CODE_FUNC is designed to be executed within a script as the main program.

10. **Item Specification**: 
    - TASK specifies "2 tomatoes," while CODE_FUNC refers to "two items" generically without specifying their nature. 

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and contexts within the domain of robotic task execution.

Refined Code Generated:
-------------------
import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment
from skill_code import pick, place
from video import init_video_writers, recording_step, recording_get_observation
from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        _, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define object positions
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        
        # === Execute Task Plan ===
        # Step 1: Pick item1 from the table
        print("[Task] Picking item1 from the table.")
        obs, reward, done = pick(env, task, target_pos=item1_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item1!")
            return
        
        # Step 2: Place item1 onto the plate
        print("[Task] Placing item1 onto the plate.")
        plate_pos = positions['plate']
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing item1!")
            return
        
        # Step 3: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, reward, done = pick(env, task, target_pos=item2_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item2!")
            return
        
        # Step 4: Place item2 onto the plate
        print("[Task] Placing item2 onto the plate.")
        obs, reward, done = place(env, task, target_pos=plate_pos, approach_distance=0.15)
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")

if __name__ == "__main__":
    run_skeleton_task()
-------------------

