=== Instruction 1 ===
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 directly.
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 (what to do), whereas CODE_FUNC provides a low-level implementation (how to do it).

3. **Context**: 
   - TASK is presented in the context of a planning domain (PDDL), focusing on the actions and states of objects. CODE_FUNC is in the context of a programming function, detailing the procedural steps to execute the task.

4. **Structure**: 
   - TASK is structured in a formalized way using PDDL syntax, including definitions of actions, predicates, and types. CODE_FUNC is structured as a programming function with procedural code, including control flow and function calls.

5. **Execution**: 
   - TASK does not execute any actions; it merely specifies them. CODE_FUNC includes executable code that performs the actions defined in TASK.

6. **Feedback Mechanism**: 
   - TASK does not provide feedback on the execution of actions. CODE_FUNC includes checks for task completion and prints messages based on the execution state.

7. **Environment Setup**: 
   - TASK assumes an environment defined by PDDL, while CODE_FUNC explicitly sets up the environment and may include additional configurations (like video recording).

8. **Termination**: 
   - TASK does not specify how to handle the end of the task. CODE_FUNC includes logic to shut down the environment properly after task execution.

9. **Item Handling**: 
   - TASK specifies the action of placing items on a plate without detailing the process. CODE_FUNC describes the iterative process of picking and placing items, including checks after each action.

10. **Directness**: 
    - TASK is a direct command to a robot, while CODE_FUNC is an indirect representation of that command through programming logic. 

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and operate at different levels of abstraction and context.

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 ===
        # 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 on the plate
        print("[Task] Placing item1 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("[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 on the plate
        print("[Task] Placing item2 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("[Task] Task ended after placing item2!")
            return

        # Step 5: Pick item3 from the table
        print("[Task] Picking item3 from the table.")
        obs, reward, done = pick(env, task, target_pos=item3_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 item3!")
            return

        # Step 6: Place item3 on the plate
        print("[Task] Placing item3 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("[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 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, then 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 directly.
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.

3. **Context**:
   - TASK is presented in a PDDL format, which is used for planning in AI.
   - CODE_FUNC is described in a programming context, likely in a specific programming language, focusing on simulation.

4. **Execution**:
   - TASK is a declarative statement that does not specify how the action is to be executed.
   - CODE_FUNC is an imperative description that includes procedural steps for execution.

5. **Components**:
   - TASK focuses solely on the action of dropping tomatoes.
   - CODE_FUNC includes multiple actions (picking and placing) and checks for task completion.

6. **Output**:
   - TASK does not specify any output or feedback mechanism.
   - CODE_FUNC mentions printing messages and checking task completion, indicating feedback to the user.

7. **Environment Setup**:
   - TASK does not mention any setup or initialization.
   - CODE_FUNC includes environment setup and resetting to an initial state.

8. **Simulation Aspect**:
   - TASK does not imply any simulation or iterative process.
   - CODE_FUNC explicitly states that it runs a simulation task.

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

10. **Execution Trigger**:
    - TASK is a standalone instruction.
    - CODE_FUNC specifies that it is executed when the script is run directly, indicating a specific trigger for execution.

Refined Code Generated:
-------------------
import numpy as np
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']
        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. It optionally initializes video recording for the simulation. The function retrieves the positions of three items and a plate. 

The task consists of a series of steps where the program picks each item in sequence and places it on the plate. After each pick and place action, it 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a specific action to be performed by a robot in a planning domain, while CODE_FUNC describes a function that implements a simulation of that task.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, focusing on actions, preconditions, and effects, whereas CODE_FUNC is written in a programming language (likely Python) and outlines a procedural approach to executing the task.
3. **Level of Abstraction**: TASK operates at a higher level of abstraction, defining what needs to be done (the planning problem), while CODE_FUNC provides a concrete implementation of how to perform those actions in a simulation.
4. **Execution**: TASK does not specify how the actions are executed or in what order, while CODE_FUNC explicitly details the sequence of actions (picking and placing items) and includes checks for task completion.
5. **Environment Setup**: TASK includes the definition of the domain and problem setup in PDDL, while CODE_FUNC describes the initialization of the environment and the resetting of the task state in a programming context.
6. **Feedback Mechanism**: TASK does not include any feedback mechanism for task completion, while CODE_FUNC includes print statements to indicate when the task is completed or if it exits early.
7. **Recording**: CODE_FUNC mentions the optional initialization of video recording for the simulation, which is not present in TASK.
8. **Direct Execution**: CODE_FUNC specifies that the function is executed when the script is run directly, which is not applicable to TASK as it is a definition rather than an executable script.

Refined Code Generated:
-------------------
import numpy as np
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 Task Plan ===
        for item_pos in [item1_pos, item2_pos, item3_pos]:
            print(f"[Task] Picking item at: {item_pos}")
            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("[Task] Task ended after picking an item!")
                return

            print(f"[Task] Placing item on the plate at: {plate_pos}")
            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)
                return

    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, then 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a specific instruction for transferring tomatoes, while CODE_FUNC describes a function that simulates the execution of such tasks.
2. **Structure**: TASK is structured in PDDL format, focusing on planning and actions, whereas CODE_FUNC is a narrative description of a procedural function.
3. **Detail Level**: TASK includes specific predicates and actions related to the robot's capabilities, while CODE_FUNC provides a high-level overview of the simulation process without detailing the underlying logic or actions.
4. **Execution Context**: TASK is a formal representation meant for planning systems, while CODE_FUNC is intended for execution in a programming environment.
5. **Focus on Objects**: TASK explicitly mentions "2 tomatoes" as the objects of interest, while CODE_FUNC refers to "two items" generically without specifying their nature.
6. **Outcome Specification**: TASK outlines the desired outcome in terms of state changes in the environment, while CODE_FUNC describes procedural steps without explicitly stating the final state.
7. **Action Sequence**: TASK defines actions in terms of preconditions and effects, while CODE_FUNC describes a sequence of actions in a more narrative form without formal preconditions or effects.
8. **Environment Setup**: TASK includes the initial state of the environment in a formal way, while CODE_FUNC describes the setup in a more informal, procedural manner.

In summary, the semantic differences between TASK and CODE_FUNC are significant, reflecting their different purposes and formats.

Refined Code Generated:
-------------------
import numpy as np
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']
        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 objects. It begins by setting up the environment and resetting the task to its initial state. It initializes video recording capabilities and retrieves the positions of specific objects. The task consists of four main steps: picking the first item from a table, placing it onto a plate, picking a second item from the table, and placing it onto the same plate. 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning domain and problem for a robot to perform actions, while CODE_FUNC describes a function that simulates the execution of those actions in a programming context.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, focusing on actions, preconditions, and effects, whereas CODE_FUNC is written in a programming language (likely Python) and outlines a procedural approach to executing the task.
3. **Execution**: TASK does not execute actions; it merely defines them for a planner to use. CODE_FUNC actually runs a simulation of the defined actions.
4. **Environment Setup**: TASK specifies the initial state and conditions for a planning problem, while CODE_FUNC includes steps for setting up the environment and resetting it before execution.
5. **Action Sequence**: TASK outlines the actions in a declarative manner (pick and place), while CODE_FUNC describes a specific sequence of actions to be performed in a procedural manner.
6. **Feedback Mechanism**: TASK does not provide feedback or messages about the execution of actions, whereas CODE_FUNC includes print statements to indicate the progress and completion of the task.
7. **Recording**: CODE_FUNC mentions initializing video recording capabilities, which is not present in TASK.
8. **Completion Check**: CODE_FUNC includes checks to determine if the task is completed after each action, while TASK does not include any such mechanism.
9. **Shutdown Procedure**: CODE_FUNC ensures proper shutdown of the environment after task execution, which is not addressed in TASK.

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 the 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.")
        plate_pos = positions['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 =====")
-------------------

