=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving object manipulation. 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, including two tomatoes, a plate, and a switch.

The task consists of a series of steps: first, it closes a gripper, then presses a switch to turn on a light, picks up the first tomato, places it on a plate, picks up the second tomato, and finally places it on the plate as well. After each action, it checks if the task is completed and prints appropriate messages. If the task ends at any step, it exits early. Finally, it ensures that the environment is properly shut down after the task is completed or if an error occurs.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning domain and problem for a robot to manipulate objects, while CODE_FUNC describes a function that simulates the execution of a task involving object manipulation.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, which includes definitions of actions, predicates, and initial states. CODE_FUNC is a narrative description of a procedural function that outlines steps for simulation.
3. **Actions**: TASK specifies actions (pick, place, close_gripper, press) with preconditions and effects, while CODE_FUNC describes a sequence of actions without formal preconditions or effects.
4. **Environment Setup**: TASK includes a detailed setup of the environment using PDDL syntax, whereas CODE_FUNC mentions setting up the environment in a more general sense without specific syntax.
5. **Error Handling**: CODE_FUNC includes a mechanism for early exit and error handling, which is not present in TASK.
6. **Output**: CODE_FUNC mentions printing messages after actions, while TASK does not specify any output or feedback mechanism.
7. **Simulation vs. Planning**: TASK is focused on planning and defining the conditions for actions, while CODE_FUNC is focused on simulating the execution of those actions in a specific order.

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 close_gripper, press, 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']
        switch_pos = positions['switch']

        # === Execute the Plan ===
        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing switch!")
            return

        # Step 3: Pick tomato1
        print("[Task] Picking tomato1...")
        obs, reward, done = pick(env, task, tomato1_pos)
        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, plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 5: Pick tomato2
        print("[Task] Picking tomato2...")
        obs, reward, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 6: Place tomato2 on the plate
        print("[Task] Placing tomato2 on the plate...")
        obs, reward, done = place(env, task, 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 2 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving object manipulation. 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, including two tomatoes, a plate, and a switch.

The task consists of a series of steps: first, it closes a gripper, then presses a switch to turn on a light, picks up the first tomato, places it on a plate, picks up the second tomato, and finally places it on the plate as well. After each action, it checks if the task is completed and prints appropriate messages. If the task is completed successfully, it displays a reward. Regardless of the outcome, the environment is shut down at the end of the function. 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 problem in a planning domain using PDDL, focusing on the actions and states of a robot in a specific environment.
   - CODE_FUNC describes a function that simulates the execution of a series of actions in a programming context.

2. **Structure**: 
   - TASK is structured in a formal PDDL format, defining actions, preconditions, and effects.
   - CODE_FUNC is written in a programming language (likely Python), outlining a procedural function with a sequence of operations.

3. **Execution**: 
   - TASK does not execute actions; it merely specifies them for a planner to interpret.
   - CODE_FUNC executes a series of actions in a defined order, simulating the task.

4. **Environment Setup**: 
   - TASK initializes the environment through PDDL's `:init` section, defining the initial state of objects and conditions.
   - CODE_FUNC sets up the environment programmatically, including optional video recording and resetting the task state.

5. **Action Sequence**: 
   - TASK defines actions (pick, place, close_gripper, press) with specific preconditions and effects.
   - CODE_FUNC describes a specific sequence of actions (closing gripper, pressing switch, picking, and placing tomatoes) without formal preconditions and effects.

6. **Feedback Mechanism**: 
   - TASK does not provide feedback or completion checks; it is a static definition.
   - CODE_FUNC includes checks for task completion and prints messages based on the outcome of the actions.

7. **Finalization**: 
   - TASK does not include a finalization step; it is a static representation of a problem.
   - CODE_FUNC includes a shutdown procedure for the environment at the end of the function.

8. **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.

9. **Object Interaction**: 
   - TASK describes interactions in terms of predicates and effects in a logical framework.
   - CODE_FUNC describes interactions in terms of function calls and procedural steps.

10. **Language**: 
    - TASK uses a domain-specific language (PDDL) for planning.
    - CODE_FUNC uses a general-purpose programming language (likely Python) for simulation.

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 close_gripper, press, 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']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick the first tomato
        print("[Task] Picking tomato1...")
        obs, reward, done = pick(env, task, tomato1_pos)
        if done:
            print("[Task] Task ended after picking tomato1!")
            return

        # Step 4: Place the first tomato onto the plate
        print("[Task] Placing tomato1 onto the plate...")
        obs, reward, done = place(env, task, plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 5: Pick the second tomato
        print("[Task] Picking tomato2...")
        obs, reward, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 6: Place the second tomato onto the plate
        print("[Task] Placing tomato2 onto the plate...")
        obs, reward, done = place(env, task, 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 3 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving object manipulation. 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, including two tomatoes, a plate, and a switch.

The task consists of a series of steps: first, it closes a gripper, then presses a switch to turn on a light, picks up the first tomato, places it on a plate, picks up the second tomato, and finally places it on the plate as well. After each action, it checks if the task is completed and prints appropriate messages. If the task ends at any step, it exits early. Finally, it ensures that the environment is properly shut down after the task is completed or if an error occurs.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robot to perform (picking up tomatoes and placing them on a plate).
   - CODE_FUNC outlines a function that implements a simulation of the task, detailing the steps and environment setup.

2. **Level of Detail**:
   - TASK provides a high-level description of the desired outcome without implementation specifics.
   - CODE_FUNC includes detailed procedural steps, including actions like closing the gripper and pressing a switch.

3. **Context**:
   - TASK is presented in the context of a planning domain definition language (PDDL) for robotic actions.
   - CODE_FUNC is described in the context of a programming function that simulates the task.

4. **Execution Flow**:
   - TASK does not specify the order of actions or the flow of execution.
   - CODE_FUNC explicitly outlines the sequence of actions to be taken during the simulation.

5. **Error Handling**:
   - TASK does not mention any error handling or conditions for early termination.
   - CODE_FUNC includes checks for task completion and early exit conditions.

6. **Environment Setup**:
   - TASK assumes a predefined environment with objects and conditions.
   - CODE_FUNC includes steps for initializing the environment and resetting the task state.

7. **Output**:
   - TASK does not specify any output or feedback mechanism.
   - CODE_FUNC mentions printing messages based on the task's progress and completion.

8. **Simulation Aspect**:
   - TASK is a theoretical instruction without a simulation context.
   - CODE_FUNC explicitly states that it runs a simulation of the task.

9. **Object Interaction**:
   - TASK focuses on the interaction with tomatoes and a plate.
   - CODE_FUNC includes additional interactions, such as closing the gripper and pressing a switch.

10. **State Management**:
    - TASK does not address state management or transitions.
    - CODE_FUNC manages the state of the environment and the robot's actions throughout the simulation.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and provide varying levels of detail 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 close_gripper, press, 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']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick tomato1
        print("[Task] Picking up tomato1...")
        obs, reward, done = pick(env, task, tomato1_pos)
        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, plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 5: Pick tomato2
        print("[Task] Picking up tomato2...")
        obs, reward, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 6: Place tomato2 on the plate
        print("[Task] Placing tomato2 on the plate...")
        obs, reward, done = place(env, task, 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 object manipulation. 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, including two tomatoes, a plate, and a switch.

The task consists of a series of steps: first, it closes a gripper, then presses a switch to turn on a light, picks up the first tomato, places it on a plate, picks up the second tomato, and finally places it on the plate as well. After each action, it 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 to ensure proper cleanup. 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 transferring tomatoes onto a plate.
   - CODE_FUNC outlines a function that simulates the execution of a series of actions to achieve a similar goal.

2. **Structure**: 
   - TASK is presented in a PDDL format, defining a domain and problem with predicates and actions.
   - CODE_FUNC is written in a programming language format, detailing a function with procedural steps.

3. **Detail Level**: 
   - TASK provides a high-level description of the goal without detailing the steps to achieve it.
   - CODE_FUNC includes specific procedural steps and checks for task completion.

4. **Environment Setup**: 
   - TASK includes the initial state of the environment in terms of predicates.
   - CODE_FUNC describes the setup and resetting of the environment as part of the function.

5. **Action Sequence**: 
   - TASK does not specify the order of actions to be taken.
   - CODE_FUNC explicitly lists the sequence of actions (closing gripper, pressing switch, picking, and placing tomatoes).

6. **Error Handling**: 
   - TASK does not mention any error handling or early exits.
   - CODE_FUNC includes checks for task completion and early exit conditions.

7. **Output**: 
   - TASK does not specify any output or feedback mechanism.
   - CODE_FUNC includes print statements to provide feedback on the task's progress.

8. **Execution Context**: 
   - TASK is a static description meant for planning or reasoning.
   - CODE_FUNC is dynamic and meant to be executed as part of a program.

9. **Focus on Conditions**: 
   - TASK emphasizes preconditions and effects in a logical format.
   - CODE_FUNC focuses on procedural execution without explicitly detailing logical conditions.

10. **Language**: 
    - TASK uses a domain-specific language (PDDL).
    - CODE_FUNC uses a general-purpose programming language syntax.

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

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

from env import setup_environment, shutdown_environment
from skill_code import close_gripper, press, 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']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick the first tomato
        print("[Task] Picking tomato1...")
        obs, reward, done = pick(env, task, tomato1_pos)
        if done:
            print("[Task] Task ended after picking tomato1!")
            return

        # Step 4: Place the first tomato on the plate
        print("[Task] Placing tomato1 on the plate...")
        obs, reward, done = place(env, task, plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 5: Pick the second tomato
        print("[Task] Picking tomato2...")
        obs, reward, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 6: Place the second tomato on the plate
        print("[Task] Placing tomato2 on the plate...")
        obs, reward, done = place(env, task, 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 =====")

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 object manipulation. 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, including two tomatoes, a plate, and a switch.

The task consists of a series of steps: first, it closes a gripper, then presses a switch to turn on a light, picks up the first tomato, places it on a plate, picks up the second tomato, and finally places it on the plate as well. After each action, it checks if the task is completed and prints appropriate messages. If the task ends at any step, it exits early. Finally, it ensures that the environment is properly shut down after the task is completed or if an error occurs.
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 a series of actions to achieve a task.

2. **Structure**: 
   - TASK is defined in PDDL format, focusing on planning and action definitions.
   - CODE_FUNC is a narrative description of a procedural function, detailing the steps taken in a simulation.

3. **Action Specification**: 
   - TASK includes detailed preconditions and effects for actions like `pick` and `place`.
   - CODE_FUNC describes a sequence of actions without specifying preconditions or effects.

4. **Environment Setup**: 
   - TASK includes a domain and problem definition with initial conditions.
   - CODE_FUNC mentions setting up the environment but does not define it in a formal way.

5. **Error Handling**: 
   - TASK does not mention error handling or early exits.
   - CODE_FUNC explicitly states that it checks for task completion and handles errors.

6. **Output**: 
   - TASK does not specify any output or feedback mechanism.
   - CODE_FUNC includes printing messages based on the task's progress and completion.

7. **Focus on Light**: 
   - TASK does not mention the light or the switch.
   - CODE_FUNC includes pressing a switch to turn on a light as part of the task.

8. **Action Order**: 
   - TASK specifies the action of tossing tomatoes but does not detail the order of actions.
   - CODE_FUNC provides a specific sequence of actions to be performed.

9. **Object Interaction**: 
   - TASK focuses on the interaction of the robot with tomatoes and a plate.
   - CODE_FUNC includes additional interactions, such as closing the gripper and pressing a switch.

10. **Simulation Context**: 
    - TASK is a theoretical instruction for a robot.
    - CODE_FUNC is framed within a simulation context, indicating a practical implementation.

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

Refined Code Generated:
-------------------
import numpy as np
from env import setup_environment, shutdown_environment
from skill_code import close_gripper, press, 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']
        
        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Pick the first tomato
        print("[Task] Picking tomato1...")
        obs, reward, done = pick(env, task, tomato1_pos)
        if done:
            print("[Task] Task ended after picking tomato1!")
            return

        # Step 3: Place the first tomato on the plate
        print("[Task] Placing tomato1 on the plate...")
        obs, reward, done = place(env, task, plate_pos)
        if done:
            print("[Task] Task ended after placing tomato1!")
            return

        # Step 4: Pick the second tomato
        print("[Task] Picking tomato2...")
        obs, reward, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 5: Place the second tomato on the plate
        print("[Task] Placing tomato2 on the plate...")
        obs, reward, done = place(env, task, 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 =====")
-------------------

