=== 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 initializes video recording capabilities to capture 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 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 written in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems, whereas CODE_FUNC is a narrative description of a programming function.
3. **Detail Level**: TASK includes detailed specifications of actions, preconditions, and effects in a formalized manner, while CODE_FUNC provides a high-level overview of the steps involved in executing the task without formal definitions.
4. **Execution Context**: TASK is a declarative representation of a problem to be solved, while CODE_FUNC is an imperative description of how to execute the task programmatically.
5. **Components**: TASK includes definitions of types, predicates, and actions, while CODE_FUNC focuses on the procedural steps and logic to perform the task.
6. **Output**: TASK does not specify any output or feedback mechanism, while CODE_FUNC mentions printing messages to indicate task completion.
7. **Environment Setup**: TASK assumes an environment defined by PDDL, while CODE_FUNC explicitly mentions setting up the environment and video recording capabilities.
8. **Action Sequence**: TASK outlines the actions in a general sense, while CODE_FUNC specifies the sequence of actions taken during the simulation in a step-by-step manner.

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

=== 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, 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 domain definitions, predicates, and actions, while CODE_FUNC includes procedural steps and function calls.
4. **Execution**: TASK is meant to be processed by a planner to generate a sequence of actions, while CODE_FUNC is executed directly as a script.
5. **State Representation**: TASK uses predicates to represent the state of the world, while CODE_FUNC uses variables and function calls to manage state.
6. **Action Specification**: TASK specifies actions with preconditions and effects, while CODE_FUNC describes actions in a step-by-step manner without formal preconditions and effects.
7. **Output**: TASK does not produce output directly; it is used for planning. CODE_FUNC includes print statements to provide feedback during execution.
8. **Environment Setup**: TASK defines the initial state and objects in a static manner, while CODE_FUNC may include dynamic setup and teardown of the environment.
9. **Flexibility**: TASK is more abstract and can be adapted to different scenarios by changing the PDDL definitions, while CODE_FUNC is specific to the defined simulation 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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Task Plan Execution ===
        # 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: first, it picks up the first tomato, then places it on the plate. Next, it picks up the second tomato 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning problem in PDDL for a robot to pick up tomatoes and place them on a plate, while CODE_FUNC describes a function that simulates the execution of that task in a programming environment.
2. **Format**: TASK is written in PDDL (Planning Domain Definition Language), which is a formal language for describing planning problems, whereas CODE_FUNC is written in a programming language (likely Python or similar) that implements the task.
3. **Structure**: TASK includes definitions of actions, predicates, and the initial state of the problem, while CODE_FUNC outlines a procedural approach to executing the task with specific steps and checks.
4. **Execution**: TASK is a declarative representation that does not execute actions but defines how they should be executed, while CODE_FUNC is an imperative representation that actually performs the actions in a simulation.
5. **Environment Setup**: TASK specifies the environment and objects in a static manner, while CODE_FUNC includes dynamic elements such as resetting the environment and initializing video recording.
6. **Feedback Mechanism**: TASK does not provide feedback or messages about the task's progress, while CODE_FUNC includes print statements to indicate the completion of actions and the overall task.
7. **Completion Check**: TASK does not include a mechanism to check if the task is completed, whereas CODE_FUNC explicitly checks for task completion after each action.
8. **Execution Context**: TASK is designed to be used in a planning system, while CODE_FUNC is intended to be run as a standalone script or function in a programming environment.

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
        print("[Task] Picking up tomato1...")
        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
        print("[Task] Picking up tomato2...")
        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 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 on the plate. Next, it picks the second tomato 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 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 implements a simulation of that task.
2. **Structure**: TASK is written in PDDL (Planning Domain Definition Language) format, whereas CODE_FUNC is a description of a procedural function in a programming context.
3. **Detail Level**: TASK provides a high-level description of the task without implementation details, while CODE_FUNC includes specific steps and actions taken during the simulation.
4. **Execution**: TASK is a declarative representation of a problem to be solved, while CODE_FUNC is an executable function that performs the task.
5. **Environment Setup**: TASK includes the definition of the domain and problem setup in PDDL, while CODE_FUNC describes the procedural setup and execution of the task in a programming environment.
6. **Feedback Mechanism**: TASK does not specify feedback or output, while CODE_FUNC mentions printing messages to indicate task completion.
7. **State Management**: TASK defines the initial state and effects of actions in a logical framework, whereas CODE_FUNC describes the procedural management of state through function calls and checks.
8. **Action Specification**: TASK specifies actions in terms of preconditions and effects, while CODE_FUNC outlines the sequence of actions in a step-by-step manner without formal preconditions and effects.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they represent different aspects of the same underlying 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
        tomato1_pos = positions['tomato1']
        tomato2_pos = positions['tomato2']
        plate_pos = positions['plate']

        # === Execute the Plan ===
        # Step 1: Pick tomato1
        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
        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 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 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).
   - CODE_FUNC outlines a function that simulates the execution of the task, detailing the steps involved in picking and placing the tomatoes.

2. **Level of Abstraction**: 
   - TASK is a high-level instruction without implementation details.
   - CODE_FUNC provides a low-level implementation, including the sequence of actions and checks.

3. **Execution Context**: 
   - TASK is a standalone instruction that can be interpreted by a robot or system.
   - CODE_FUNC is part of a programming script that requires a runtime environment to execute.

4. **Detailing of Actions**: 
   - TASK simply states the action to be performed (tossing).
   - CODE_FUNC breaks down the actions into specific steps (picking and placing each tomato).

5. **Feedback Mechanism**: 
   - TASK does not include any feedback or confirmation of completion.
   - CODE_FUNC includes checks for task completion and prints messages accordingly.

6. **Environment Setup**: 
   - TASK does not specify how the environment is set up.
   - CODE_FUNC includes details about initializing the environment and resetting the task state.

7. **Recording**: 
   - TASK does not mention any recording or monitoring.
   - CODE_FUNC mentions optional video recording for the simulation.

8. **Execution Trigger**: 
   - TASK is an instruction that can be executed at any time.
   - CODE_FUNC is executed when the script is run as the main program.

9. **Object Interaction**: 
   - TASK implies interaction with objects but does not detail the process.
   - CODE_FUNC explicitly describes the interaction with each tomato and the plate.

10. **Completion Handling**: 
    - TASK does not address what happens after the action is performed.
    - CODE_FUNC includes a mechanism to ensure proper shutdown of the environment after task 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()
-------------------

