=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing an object. 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 various objects, specifically two tomatoes and a bin. 

The task consists of two main steps: first, it picks up the second tomato from its designated position, and if successful, it then places that tomato into the bin. 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 describes a high-level instruction for a robot to perform ("put rubbish in bin"), while CODE_FUNC details the implementation of a simulation that executes this task.

2. **Structure**: 
   - TASK is presented in a formal PDDL (Planning Domain Definition Language) format, defining a domain and problem setup, whereas CODE_FUNC is a narrative description of a function that simulates the task.

3. **Level of Abstraction**: 
   - TASK operates at a conceptual level, focusing on what needs to be done, while CODE_FUNC operates at a procedural level, detailing how the task is executed programmatically.

4. **Components**: 
   - TASK includes definitions of actions, predicates, and initial conditions in a planning context, while CODE_FUNC includes procedural steps, such as initializing the environment, picking up objects, and checking task completion.

5. **Execution Context**: 
   - TASK is designed for a planning system that interprets PDDL, while CODE_FUNC is intended for execution in a programming environment, likely involving a simulation framework.

6. **Feedback Mechanism**: 
   - TASK does not specify feedback or output mechanisms, while CODE_FUNC explicitly mentions printing messages and checking task completion.

7. **Environment Setup**: 
   - TASK defines the initial state of the world in terms of predicates, whereas CODE_FUNC describes the procedural steps to set up the environment and reset the task.

8. **Error Handling**: 
   - TASK does not address error handling or success conditions, while CODE_FUNC includes checks for successful actions and task completion.

9. **Execution Flow**: 
   - TASK does not specify the order of actions beyond the high-level instruction, while CODE_FUNC outlines a specific sequence of actions (pick, then place) and their conditions.

10. **Simulation Aspect**: 
    - TASK is purely about the task itself, while CODE_FUNC includes elements of simulation, such as video recording and environment shutdown.

11. **Object Interaction**: 
    - TASK focuses on the interaction of the robot with objects in a general sense, while CODE_FUNC specifies which objects are involved (e.g., "second tomato" and "bin") and their specific roles in the simulation.

12. **Main Program Execution**: 
    - TASK does not mention how it is executed or triggered, while CODE_FUNC specifies that it runs when the script is executed as the main program.

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
        tomato2_pos = positions['tomato2']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        # Step 1: Pick tomato2
        print("[Task] Picking tomato2 at:", tomato2_pos)
        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 2: Place tomato2 in the bin
        print("[Task] Placing tomato2 in bin at:", bin_pos)
        obs, reward, done = place(
            env,
            task,
            target_pos=bin_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 an object. 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 and a bin. 

The task consists of two main steps: first, it picks up a specified tomato from a table, and if successful, it then places that tomato into a bin. 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 describes a high-level instruction for a robot to perform (drop rubbish into the bin), while CODE_FUNC outlines a specific implementation of a simulation task involving picking and placing objects.
2. **Structure**: TASK is presented in a formal PDDL (Planning Domain Definition Language) format, defining a domain and problem, whereas CODE_FUNC is described in a narrative format detailing the function's operations.
3. **Detail Level**: TASK provides a structured representation of actions, preconditions, and effects, while CODE_FUNC gives a procedural description of how the task is executed in a simulation context.
4. **Context**: TASK is focused on the planning aspect of robotic actions, while CODE_FUNC emphasizes the execution and simulation of those actions.
5. **Components**: TASK includes definitions of predicates and actions in a domain, while CODE_FUNC includes procedural steps, such as initializing the environment and checking task completion.
6. **Execution**: TASK does not specify how the actions are executed, whereas CODE_FUNC describes the execution flow, including success checks and environment shutdown.
7. **Output**: TASK does not mention any output or feedback mechanism, while CODE_FUNC includes printing relevant messages after actions are performed.

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
        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
        tomato2_pos = positions['tomato2']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        # Step 1: 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 2: Place tomato2 into the bin
        print("[Task] Placing tomato2 into the bin.")
        obs, reward, done = place(
            env,
            task,
            target_pos=bin_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 initializes video recording capabilities and retrieves the positions of specific objects. The task consists of a series of steps where the program picks up three items (two tomatoes and one other item) from a table and places them into a bin. After each action, it checks if the task is complete 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a specific action to be performed by a robot in a planning domain, while CODE_FUNC describes a function that simulates the execution of that task in a programming environment.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, focusing on actions, preconditions, and effects, whereas CODE_FUNC is written in a programming language (likely Python) and describes procedural steps.
3. **Level of Abstraction**: TASK operates at a higher level of abstraction, defining the logical framework for robot actions, while CODE_FUNC provides a concrete implementation of those actions in a simulation.
4. **Execution Context**: TASK is meant for planning and reasoning about actions in a domain, while CODE_FUNC is intended for executing a simulation of those actions in a specific environment.
5. **Output**: TASK does not specify any output or feedback mechanism, while CODE_FUNC includes print statements to provide feedback on the task's progress and completion.
6. **Initialization**: TASK initializes the state of the world through PDDL constructs, while CODE_FUNC initializes the environment and sets up video recording capabilities programmatically.
7. **Action Sequence**: TASK defines the actions (pick and place) in a general sense, while CODE_FUNC specifies the sequence of actions to be taken (picking up three items) in a specific order.
8. **Environment Management**: TASK does not address environment management, while CODE_FUNC includes steps to ensure proper shutdown of the environment after task 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
        _, 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
        tomato2_pos = positions['tomato2']
        item2_pos = positions['item2']
        tomato1_pos = positions['tomato1']
        bin_pos = positions['bin']

        # === Execute the Plan ===
        # Step 1: 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)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 2: Place tomato2 in the bin
        print("[Task] Placing tomato2 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing tomato2!")
            return

        # Step 3: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, reward, done = pick(env, task, target_pos=item2_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 4: Place item2 in the bin
        print("[Task] Placing item2 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing item2!")
            return

        # Step 5: 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 6: Place tomato1 in the bin
        print("[Task] Placing tomato1 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_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 4 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing an object. 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 and a bin. 

The first step of the task involves picking up the second tomato from a table, and if successful, it proceeds to the next step of placing the tomato into a bin. If either step is completed, the function prints a message indicating the task's status and exits. Finally, it ensures that the environment is properly shut down before concluding the task.
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 formal PDDL format defining a domain and problem, whereas CODE_FUNC is written in a programming language format describing a function.
3. **Detail Level**: TASK provides a high-level description of the action to be performed (throwing away trash), while CODE_FUNC includes detailed steps and logic for executing the task in a simulation environment.
4. **Context**: TASK is focused on the logical representation of actions and states in a planning domain, while CODE_FUNC is concerned with the procedural implementation of those actions in a programming context.
5. **Execution**: TASK does not specify how the actions are executed, while CODE_FUNC explicitly describes the steps taken to pick and place objects, including success checks and environment management.
6. **Output**: TASK does not produce any output or feedback, while CODE_FUNC includes print statements to indicate the status of the task.
7. **Environment Management**: TASK does not address the setup or teardown of the environment, whereas CODE_FUNC includes steps to reset and shut down the environment.
8. **Object Interaction**: TASK specifies the action of throwing away trash, while CODE_FUNC details the interaction with specific objects (e.g., picking up a tomato and placing it in a bin).

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
        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
        tomato2_pos = positions['tomato2']
        bin_pos = positions['bin']

        # === Execute the Plan ===
        # Step 1: 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 2: Place tomato2 in the bin
        print("[Task] Placing tomato2 in the bin.")
        obs, reward, done = place(
            env,
            task,
            target_pos=bin_pos,
            approach_distance=0.15,
            max_steps=100,
            threshold=0.01,
            approach_axis='z',
            timeout=10.0
        )
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
            return

    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")
-------------------

=== Instruction 5 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving picking and placing objects. It begins by setting up the environment and resetting the task to its initial state. It initializes video recording capabilities and retrieves the positions of specific objects. The task consists of a series of steps where the program picks up three items (two tomatoes and one other item) from a table and places them into a bin. 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 directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning domain and problem for a robot disposal task, while CODE_FUNC describes a function that simulates the execution of that task.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, whereas CODE_FUNC is written in a programming language (likely Python or similar).
3. **Components**: TASK includes definitions of actions, predicates, and initial conditions, while CODE_FUNC focuses on the procedural steps to execute the task.
4. **Execution**: TASK does not execute any actions; it merely defines what actions can be taken. CODE_FUNC actually runs a simulation of those actions.
5. **Environment Setup**: TASK specifies the initial state of the environment in a declarative manner, while CODE_FUNC includes procedural steps to set up and reset the environment.
6. **Output**: TASK does not produce output; it defines conditions and effects. CODE_FUNC includes print statements to provide feedback on the task's progress and completion.
7. **Action Sequence**: TASK outlines the actions (pick and place) in a general sense, while CODE_FUNC specifies the exact sequence and conditions under which these actions are performed.
8. **State Management**: TASK uses predicates to represent the state of the world, while CODE_FUNC likely uses variables and control structures to manage state during execution.
9. **Focus**: TASK focuses on the logical representation of the problem, while CODE_FUNC focuses on the implementation and execution of the solution.

Refined Code Generated:
-------------------
import numpy as np

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
        tomato2_pos = positions['tomato2']
        item2_pos = positions['item2']
        tomato1_pos = positions['tomato1']
        bin_pos = positions['bin']

        # === Execute the Plan ===
        # Step 1: 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)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 2: Place tomato2 in the bin
        print("[Task] Placing tomato2 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing tomato2!")
            return

        # Step 3: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, reward, done = pick(env, task, target_pos=item2_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 4: Place item2 in the bin
        print("[Task] Placing item2 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing item2!")
            return

        # Step 5: 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 6: Place tomato1 in the bin
        print("[Task] Placing tomato1 in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_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 =====")

if __name__ == "__main__":
    run_skeleton_task()
-------------------

