=== Instruction 1 ===
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 tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table, then places it on the plate. Next, it picks the second tomato from the table and places it on 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 follow, while CODE_FUNC outlines a function that implements a simulation of that task.

2. **Structure**: 
   - TASK is presented in a PDDL format defining a planning domain and problem, whereas CODE_FUNC is described in a narrative format detailing the steps of a function.

3. **Level of Abstraction**: 
   - TASK operates at a higher level of abstraction, focusing on the goals and actions in a planning context. CODE_FUNC provides a lower-level implementation detail of how those actions are executed in a simulation.

4. **Execution Context**: 
   - TASK is a theoretical construct meant for planning and reasoning about actions, while CODE_FUNC is a practical implementation that runs in a programming environment.

5. **Components**: 
   - TASK includes definitions of predicates, actions, and their effects, while CODE_FUNC includes procedural steps, such as picking and placing objects, and managing the simulation environment.

6. **Output**: 
   - TASK does not specify any output or feedback mechanism, while CODE_FUNC includes checks for task completion and prints messages based on the execution of actions.

7. **Initialization**: 
   - TASK initializes the state of the world through PDDL's `:init` section, while CODE_FUNC describes the initialization of the simulation environment and optional video recording.

8. **Action Sequence**: 
   - TASK defines actions in a general sense (pick and place), while CODE_FUNC specifies the exact sequence of actions taken during the simulation (picking and placing each tomato).

9. **Environment Management**: 
   - TASK does not address how the environment is managed or reset, whereas CODE_FUNC explicitly mentions resetting the task and shutting down the environment after execution.

10. **Execution Trigger**: 
    - TASK is a standalone instruction that can be interpreted by a planner, while CODE_FUNC is executed as part of a script when run as the main program.

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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1 from the table
        print("[Task] Picking tomato1 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato1_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 tomato1!")
            return

        # Step 2: Place tomato1 on the plate
        print("[Task] Placing tomato1 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 tomato1!")
            return

        # Step 3: Pick tomato2 from the table
        print("[Task] Picking tomato2 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato2_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 tomato2!")
            return

        # Step 4: Place tomato2 on the plate
        print("[Task] Placing tomato2 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 tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table, then places it onto the plate. Next, it picks the second tomato 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 defines a planning problem in PDDL format, specifying the actions and states of a robot in a disposal scenario.
   - CODE_FUNC describes a procedural function that simulates the execution of the task defined in TASK.

2. **Format**: 
   - TASK is written in a declarative format (PDDL) focusing on the definition of actions, states, and their relationships.
   - CODE_FUNC is written in an imperative programming style, detailing step-by-step instructions for executing the task.

3. **Components**: 
   - TASK includes definitions of types, predicates, actions, and their preconditions and effects.
   - CODE_FUNC includes procedural steps, such as initializing the environment, executing actions, and checking for task completion.

4. **Execution**: 
   - TASK does not execute any actions; it merely defines what actions can be taken and under what conditions.
   - CODE_FUNC actively performs the actions of picking and placing the tomatoes, simulating the task.

5. **State Management**: 
   - TASK uses logical predicates to represent the state of the world (e.g., `at`, `holding`, `handempty`).
   - CODE_FUNC manages state through procedural logic, checking conditions and updating the state after each action.

6. **Output**: 
   - TASK does not produce output; it is a formal specification.
   - CODE_FUNC includes print statements to provide feedback on the task's progress and completion.

7. **Context**: 
   - TASK is context-free and can be used in any planner that understands PDDL.
   - CODE_FUNC is context-dependent, requiring a specific programming environment to run.

8. **Flexibility**: 
   - TASK can be adapted for different scenarios by changing the PDDL definitions.
   - CODE_FUNC is less flexible, as it is hardcoded to perform specific actions in a specific order.

9. **Error Handling**: 
   - TASK does not include any error handling mechanisms.
   - CODE_FUNC may include checks and messages to handle errors or unexpected states during execution.

10. **Initialization**: 
    - TASK assumes an initial state defined in the problem.
    - CODE_FUNC explicitly initializes the environment and resets the task state before 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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1 from the table
        print("[Task] Picking tomato1 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato1_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 tomato1!")
            return

        # Step 2: Place tomato1 onto the plate
        print("[Task] Placing tomato1 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 tomato1!")
            return

        # Step 3: Pick tomato2 from the table
        print("[Task] Picking tomato2 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato2_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 tomato2!")
            return

        # Step 4: Place tomato2 onto the plate
        print("[Task] Placing tomato2 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 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 tomatoes and a plate. 

The task consists of four main steps: picking up the first tomato, placing it on the plate, picking up the second tomato, and placing it on the 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 before ending the task.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning domain and problem for a robot to pick up and place tomatoes, while CODE_FUNC describes a function that simulates the execution of that task.
2. **Structure**: TASK is written in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems, whereas CODE_FUNC is a description of a procedural function in a programming context.
3. **Components**: TASK includes definitions of actions, predicates, and initial conditions, while CODE_FUNC outlines a sequence of operations to be performed in a simulation.
4. **Execution**: TASK does not execute any actions; it merely specifies what actions can be taken and under what conditions. CODE_FUNC describes the actual execution of those actions in a simulation environment.
5. **Environment Setup**: TASK specifies the initial state of the environment in terms of predicates, while CODE_FUNC describes the procedural setup and reset of the simulation environment.
6. **Feedback Mechanism**: TASK does not provide feedback or messages about the task's progress, while CODE_FUNC includes checks and messages to indicate the completion of actions.
7. **Simulation Aspect**: CODE_FUNC explicitly mentions video recording and simulation management, which is not present in TASK.

Overall, the two serve different purposes: TASK is for planning and defining the problem, while CODE_FUNC is for executing the defined task in a simulated environment.

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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1
        print("[Task] Picking up tomato1...")
        obs, reward, done = pick(env, task, target_pos=tomato1_pos)
        if done:
            print("[Task] Task ended after picking tomato1!")
            return

        # Step 2: Place tomato1 on the plate
        print("[Task] Placing tomato1 on the plate...")
        obs, reward, done = place(env, task, target_pos=plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 3: Pick tomato2
        print("[Task] Picking up tomato2...")
        obs, reward, done = pick(env, task, target_pos=tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 4: Place tomato2 on the plate
        print("[Task] Placing tomato2 on the plate...")
        obs, reward, done = place(env, task, target_pos=plate_pos)
        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 =====")
-------------------

=== 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 tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table, then places it onto the plate. Next, it picks the second tomato 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 defines a planning problem in PDDL format, specifying the actions and conditions for a robot to transfer tomatoes.
   - CODE_FUNC describes a procedural function that simulates the execution of the task defined in TASK.

2. **Format**: 
   - TASK is written in a declarative format (PDDL) focusing on the definition of actions, predicates, and the problem setup.
   - CODE_FUNC is written in an imperative programming style, detailing step-by-step instructions for executing the task.

3. **Level of Abstraction**: 
   - TASK abstracts the problem into a formal representation suitable for automated planning.
   - CODE_FUNC provides a concrete implementation of the task, detailing how to perform the actions programmatically.

4. **Execution**: 
   - TASK does not execute any actions; it merely defines what actions can be taken and under what conditions.
   - CODE_FUNC actively performs the actions of picking and placing the tomatoes, including checking for task completion.

5. **Environment Setup**: 
   - TASK specifies the initial state and the actions in a static manner.
   - CODE_FUNC includes dynamic elements such as video recording and environment reset, which are not present in TASK.

6. **Feedback Mechanism**: 
   - TASK does not provide any feedback or output regarding the execution of actions.
   - CODE_FUNC includes print statements to inform the user about the progress and completion of the task.

7. **Action Sequence**: 
   - TASK defines the actions in a general sense without specifying the order of execution.
   - CODE_FUNC explicitly outlines the sequence of actions to be performed (pick first tomato, place it, pick second tomato, place it).

8. **Completion Check**: 
   - TASK does not include any mechanism to check if the task is completed.
   - CODE_FUNC includes checks to determine if the task has been completed after each action.

9. **Shutdown Procedure**: 
   - TASK does not mention any shutdown or cleanup procedures.
   - CODE_FUNC ensures that the environment is properly shut down after the task execution.

10. **Context**: 
    - TASK is context-free and can be applied to any similar problem in the defined domain.
    - CODE_FUNC is context-specific, tailored to the particular simulation environment and task execution.

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

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 *
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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1 from the table
        print("[Task] Picking tomato1 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato1_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 tomato1!")
            return

        # Step 2: Place tomato1 onto the plate
        print("[Task] Placing tomato1 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 tomato1!")
            return

        # Step 3: Pick tomato2 from the table
        print("[Task] Picking tomato2 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato2_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 tomato2!")
            return

        # Step 4: Place tomato2 onto the plate
        print("[Task] Placing tomato2 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 optionally initializes video recording for the simulation. The function retrieves the positions of two tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table, then places it onto the plate. Next, it picks the second tomato 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 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 written in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems, whereas CODE_FUNC is written in a programming language (likely Python or similar) and describes procedural steps.
3. **Level of Abstraction**: TASK operates at a higher level of abstraction, focusing on the logical representation of actions and states, while CODE_FUNC provides a concrete implementation of those actions in a simulation.
4. **Execution**: TASK does not execute actions; it merely defines them and their conditions. CODE_FUNC actually performs the actions in a simulated environment.
5. **Environment Setup**: TASK specifies the initial state and conditions for the robot's actions, while CODE_FUNC includes additional details about setting up the simulation environment and managing its lifecycle (e.g., video recording, shutdown).
6. **Feedback Mechanism**: TASK does not include any feedback or output mechanisms, while CODE_FUNC includes print statements to provide feedback on the task's progress and completion.
7. **Action Sequence**: TASK defines actions in a general sense (pick and place), while CODE_FUNC specifies the exact sequence and timing of these actions in a simulation context.

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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1 from the table
        print("[Task] Picking tomato1 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato1_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 tomato1!")
            return

        # Step 2: Place tomato1 onto the plate
        print("[Task] Placing tomato1 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 tomato1!")
            return

        # Step 3: Pick tomato2 from the table
        print("[Task] Picking tomato2 from the table.")
        obs, reward, done = pick(env, task, target_pos=tomato2_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 tomato2!")
            return

        # Step 4: Place tomato2 onto the plate
        print("[Task] Placing tomato2 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()
-------------------

