=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task where an agent interacts with objects in an environment. It begins by setting up the environment and resetting the task to its initial state. The function initializes video recording capabilities to capture the simulation. It retrieves the positions of various objects, including two items and a plate.

The task consists of a series of steps where the agent picks up items from a table and places them on the plate. Specifically, the agent picks "item1," places it on the plate, picks "tomato1," places it on the plate, picks "item2," and finally places it on the plate. After each action, the function checks if the task is completed and prints appropriate messages. If the task ends at any step, it exits early. 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. **Nature of Content**: TASK is a formal representation of a planning problem in PDDL (Planning Domain Definition Language), while CODE_FUNC is a description of a procedural function that simulates the execution of a task in a programming context.
2. **Format**: TASK is structured in a domain-specific language (PDDL), which includes definitions of actions, predicates, and problem instances. CODE_FUNC is written in a narrative format that describes the behavior of a function in a programming language.
3. **Purpose**: TASK defines the rules and conditions for a robot to perform actions in a planning scenario, whereas CODE_FUNC describes the implementation of a simulation that executes those actions in a specific sequence.
4. **Level of Abstraction**: TASK operates at a higher level of abstraction, focusing on the logical relationships and constraints of actions, while CODE_FUNC provides a concrete implementation of those actions in a simulated environment.
5. **Execution**: TASK does not specify how the actions are executed; it only defines what actions can be taken under what conditions. CODE_FUNC explicitly describes the sequence of actions taken by the agent and includes checks for task completion.
6. **State Management**: TASK defines the initial state and the effects of actions in terms of predicates, while CODE_FUNC describes how the state is managed through function calls and checks during the simulation.
7. **Output**: TASK does not produce output in the traditional sense; it is a model for planning. CODE_FUNC includes print statements to provide feedback on the task's progress and completion.
8. **Environment Interaction**: TASK describes the environment in terms of objects and locations, while CODE_FUNC details how the agent interacts with these objects in a simulated environment.

In summary, the two representations serve different purposes and operate at different levels of abstraction, with TASK focusing on planning and CODE_FUNC on 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
        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']
        tomato1_pos = positions['tomato1']
        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)
        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)
        if done:
            print("[Task] Task ended after placing item1!")
            return

        # Step 3: 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)
        if done:
            print("[Task] Task ended after picking tomato1!")
            return

        # Step 4: 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)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 5: 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 6: 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)
        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 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 initializes video recording capabilities and retrieves the positions of specific objects, namely two tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table; second, it places that tomato onto the plate; third, it picks the second tomato from the table; and finally, it places the second tomato onto the plate. After each action, it checks if the task is completed and prints appropriate messages. 

Regardless of the outcome, the environment is shut down at the end of the task. 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 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 task of picking and placing objects, detailing the steps and environment setup.

2. **Format**: 
   - TASK is written in a declarative format (PDDL) focused on defining actions, preconditions, and effects.
   - CODE_FUNC is written in an imperative programming style, outlining a sequence of operations to be executed.

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 execute the actions step-by-step.

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

5. **Environment Interaction**: 
   - TASK specifies the initial state and the effects of actions but does not detail how to interact with the environment.
   - CODE_FUNC includes specific interactions with the environment, such as initializing video recording and shutting down the environment after task completion.

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

7. **Object Management**: 
   - TASK defines objects and their states in a static manner.
   - CODE_FUNC dynamically manages the state of objects as actions are performed (e.g., picking and placing).

8. **Task Completion**: 
   - TASK does not define a mechanism for determining when the task is complete.
   - CODE_FUNC includes checks to determine if the task has been completed after each action.

9. **Initialization**: 
   - TASK initializes the problem state through the PDDL format.
   - CODE_FUNC explicitly initializes the environment and resets the task state programmatically.

10. **Action Sequence**: 
    - TASK defines actions in a general sense without specifying the order of execution.
    - CODE_FUNC specifies a precise sequence of actions to be performed in a specific order.

11. **Error Handling**: 
    - TASK does not include any error handling or recovery mechanisms.
    - CODE_FUNC may implicitly handle errors through checks and feedback after each action.

12. **Context**: 
    - TASK is context-free and can be used in various planning scenarios.
    - CODE_FUNC is context-specific, tailored to a particular simulation of picking and placing tomatoes.

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 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 =====")
-------------------

=== Instruction 3 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking up an object and placing it in a designated location. 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 specific objects, namely a tomato and a plate. 

The task consists of two main steps: first, it picks up the tomato from its initial position, and if successful, it then places the tomato onto the plate. After each action, it checks if the task is completed and prints relevant 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 specific instruction for a robot to follow, while CODE_FUNC describes a function that implements a simulation of that instruction.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems, whereas CODE_FUNC is a description of a programming function that executes the task.
3. **Level of Abstraction**: TASK operates at a higher level of abstraction, focusing on the goals and actions in a planning context, while CODE_FUNC provides a lower-level implementation detail of how to achieve those goals programmatically.
4. **Execution Context**: TASK is a theoretical construct that can be used in planning algorithms, while CODE_FUNC is an executable piece of code 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 initializing the environment, checking conditions, and printing messages.
6. **Outcome Handling**: TASK does not specify how to handle outcomes or errors, while CODE_FUNC includes checks for task completion and prints relevant messages based on the success or failure of actions.
7. **Environment Interaction**: TASK describes the robot's actions in terms of state changes in a planning domain, while CODE_FUNC describes the interaction with the environment in a simulation context, including setup and teardown processes.

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']
        plate_pos = positions['plate']

        # === Task Plan Execution ===
        # Step 1: Pick up the tomato1
        print("[Task] Picking up tomato1 at:", tomato1_pos)
        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 the tomato1 on the plate
        print("[Task] Placing tomato1 on 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)
        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 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 initializes video recording capabilities to capture the simulation. The function retrieves the positions of objects, specifically two tomatoes and a plate. 

The task consists of four main steps: first, it picks the first tomato from the table; second, it places that tomato onto the plate; third, it picks the second tomato from the table; and finally, it places the second tomato onto the plate. After each action, it checks if the task is completed and prints appropriate messages. 

Regardless of the outcome, the environment is shut down at the end of the task. 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 instruction for transferring tomatoes, while CODE_FUNC describes a function that implements a simulation of that task.
2. **Structure**: TASK is presented in a formal PDDL format, while CODE_FUNC is written in a programming language (likely Python or similar).
3. **Detail Level**: TASK provides a high-level description of the action to be performed, whereas CODE_FUNC includes detailed steps and procedural logic for executing the task.
4. **Execution**: TASK does not specify how the actions are executed, while CODE_FUNC explicitly outlines the sequence of actions and checks for task completion.
5. **Environment Setup**: TASK assumes a predefined environment, while CODE_FUNC includes steps for setting up and resetting the environment.
6. **Recording**: CODE_FUNC mentions video recording capabilities, which is not addressed in TASK.
7. **Completion Check**: CODE_FUNC includes checks for task completion and prints messages, while TASK does not include any feedback mechanism.
8. **Shutdown Procedure**: CODE_FUNC specifies shutting down the environment at the end, which is not mentioned in TASK.

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 =====")
-------------------

=== 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; second, it places that tomato onto the plate; third, it picks the second tomato from the table; and finally, it places the second tomato onto the plate. After each action, it checks if the task is completed and prints appropriate messages. 

Regardless of the outcome, the environment is shut down at the end of the task. 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, while CODE_FUNC describes a procedural function for simulating the task.
2. **Format**: TASK is structured in a declarative format (PDDL), whereas CODE_FUNC is written in an imperative programming style.
3. **Components**: TASK includes definitions of actions, predicates, and initial states, while CODE_FUNC outlines a sequence of operations to be executed.
4. **Execution**: TASK does not specify how the actions are executed; it only defines the conditions and effects. CODE_FUNC explicitly describes the steps taken to perform the task.
5. **Environment Interaction**: TASK focuses on the logical representation of the problem, while CODE_FUNC involves direct interaction with the environment (e.g., picking and placing objects).
6. **Output**: TASK does not produce output messages; it defines a problem. CODE_FUNC includes print statements to indicate the progress of the task.
7. **State Management**: TASK defines the initial state and effects of actions in a logical manner, while CODE_FUNC manages state through procedural steps and checks after each action.
8. **Simulation Aspect**: TASK is a theoretical representation, while CODE_FUNC is designed to run a simulation of the task in a practical environment. 

These differences highlight the distinction between a formal problem definition and a practical implementation of that problem.

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 the first tomato
        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 the first tomato 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 the second tomato
        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 the second tomato 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 =====")
-------------------

