=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task where a robotic agent picks up three items from a table and places them into a bin. It begins by setting up the environment and resetting the task to its initial state. Video recording is optionally initialized to capture the simulation. The positions of the items and the bin are retrieved.

The task consists of six main steps: picking each item from the table and placing it into the bin. After each pick and place action, the code checks if the task is completed. If the task ends after any step, a message is printed, and the function 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. **Purpose**: 
   - TASK describes a high-level instruction for a robot to perform ("put rubbish in bin"), while CODE_FUNC outlines a specific implementation of that task in a simulation environment.

2. **Level of Abstraction**: 
   - TASK is an abstract directive without implementation details, whereas CODE_FUNC provides a concrete procedural breakdown of how to execute the task.

3. **Structure**: 
   - TASK is presented in a natural language format, while CODE_FUNC is structured as a programming function with defined steps and logic.

4. **Execution Context**: 
   - TASK does not specify how the task is to be executed or the environment setup, while CODE_FUNC explicitly mentions setting up the environment and resetting the task state.

5. **Detailing of Steps**: 
   - TASK does not enumerate the steps involved in completing the instruction, while CODE_FUNC details the six main steps involved in the simulation.

6. **Feedback Mechanism**: 
   - TASK does not include any feedback or completion checks, whereas CODE_FUNC includes checks to determine if the task is completed after each action.

7. **Error Handling**: 
   - TASK does not address error handling or contingencies, while CODE_FUNC implies a structured approach to managing task execution and shutdown.

8. **Output**: 
   - TASK does not specify any output or result of the action, while CODE_FUNC mentions printing messages upon task completion.

9. **Environment Interaction**: 
   - TASK does not describe interactions with the environment, while CODE_FUNC explicitly mentions retrieving positions and managing the environment.

10. **Execution Trigger**: 
    - TASK is a standalone instruction, while CODE_FUNC includes a condition for execution (i.e., when the script is run 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
        _, 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']
        item3_pos = positions['item3']
        bin_pos = positions['bin']

        # === Execute Task 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,
            max_steps=100,
            threshold=0.01,
            approach_axis='z',
            timeout=10.0
        )
        if done:
            print("[Task] Task ended after picking item1!")
            return

        # Step 2: Place item1 in the bin
        print("[Task] Placing item1 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 ended after placing item1!")
            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,
            max_steps=100,
            threshold=0.01,
            approach_axis='z',
            timeout=10.0
        )
        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,
            max_steps=100,
            threshold=0.01,
            approach_axis='z',
            timeout=10.0
        )
        if done:
            print("[Task] Task ended after placing item2!")
            return

        # Step 5: Pick item3 from the table
        print("[Task] Picking item3 from the table.")
        obs, reward, done = pick(
            env,
            task,
            target_pos=item3_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 item3!")
            return

        # Step 6: Place item3 in the bin
        print("[Task] Placing item3 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)
        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 items. It begins by setting up the environment and resetting the task to its initial state. Video recording is optionally initialized to capture the simulation. The positions of three items and a bin are retrieved. 

The task consists of a series of steps where the program attempts to pick each item from a table and place it into the bin. For each item, it first picks the item, checks if the task is completed, and then places the item into the bin, again checking for task completion. If the task ends after picking any item, it prints a message and exits. 

Finally, the environment is shut down properly, and the function concludes with a message indicating 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 action to be performed (dropping rubbish into the bin), while CODE_FUNC describes a function that simulates a series of actions to achieve that purpose.
2. **Structure**: TASK is structured in PDDL format, focusing on the planning domain and problem definition, 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 actions and their effects in a planning context, while CODE_FUNC provides a concrete implementation of those actions in a simulation.
4. **Execution**: TASK does not specify how the actions are executed, only the conditions and effects, while CODE_FUNC includes the actual execution flow, including checks for task completion and environment management.
5. **Environment Setup**: TASK includes the initial state of the environment in a declarative manner, while CODE_FUNC describes the procedural setup and resetting of the environment.
6. **Feedback Mechanism**: TASK does not provide feedback or messages about the task's progress, while CODE_FUNC includes print statements to indicate task completion and the end of the task.
7. **Control Flow**: TASK does not include any control flow or decision-making processes, while CODE_FUNC includes logic to check for task completion and handle the flow of actions based on that condition.

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
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        item3_pos = positions['item3']
        bin_pos = positions['bin']

        # === Execute Task 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, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item1!")
            return

        # Step 2: Place item1 into the bin
        print("[Task] Placing item1 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)
            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, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 4: Place item2 into the bin
        print("[Task] Placing item2 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)
            return

        # Step 5: Pick item3 from the table
        print("[Task] Picking item3 from the table.")
        obs, reward, done = pick(env, task, target_pos=item3_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 item3!")
            return

        # Step 6: Place item3 into the bin
        print("[Task] Placing item3 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)
            return

    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 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 task involves picking three items from a table and placing them into a designated bin. 

For each item, the code retrieves its position, executes a pick action, and then executes a place action into the bin. After each action, it checks if the task is completed and prints appropriate messages. The simulation can also record video of the task execution. Finally, the environment is shut down properly after the task is completed or if an early exit occurs. 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, while CODE_FUNC outlines a function that implements a simulation of that task.
2. **Level of Abstraction**: TASK is a high-level directive, whereas CODE_FUNC provides a detailed procedural implementation of the task.
3. **Context**: TASK is presented in a planning domain definition language (PDDL) format, while CODE_FUNC is described in a programming context, likely in a programming language.
4. **Components**: TASK includes domain definitions, predicates, and actions, while CODE_FUNC includes procedural steps, function calls, and simulation management.
5. **Execution**: TASK does not specify how the actions are executed, while CODE_FUNC explicitly describes the execution of actions and checks for task completion.
6. **Output**: TASK does not mention any output or feedback mechanism, whereas CODE_FUNC includes printing messages and potentially recording video.
7. **Environment Management**: TASK does not address environment setup or teardown, while CODE_FUNC includes steps for initializing and shutting down the environment.
8. **Item Handling**: TASK specifies the action of picking up rubbish and placing it in a trash can, while CODE_FUNC details the process of picking multiple items and placing them into a bin.
9. **Iteration**: TASK is a single instruction, while CODE_FUNC involves iterating over multiple items to perform the pick and place actions.
10. **Error Handling**: TASK does not include any error handling, while CODE_FUNC implies the possibility of early exits and proper shutdown procedures.

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']
        item3_pos = positions['item3']
        bin_pos = positions['bin']

        # === Execute Task Plan ===
        for item_pos in [item1_pos, item2_pos, item3_pos]:
            print(f"[Task] Picking item at position: {item_pos}")
            obs, reward, done = pick(
                env,
                task,
                target_pos=item_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 an item!")
                return

            print(f"[Task] Placing item in the 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)
                return

    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 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 simulation can optionally record video of the task execution. 

The agent retrieves the positions of three items and a bin. It then follows a sequence of steps: picking each item from the table and placing it into the bin. After each pick and place action, the code checks if the task is completed and prints appropriate messages. 

Finally, the environment is shut down to ensure proper cleanup 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 a robot to follow, while CODE_FUNC describes a function that simulates the execution of that instruction in a programming context.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, focusing on actions, predicates, and problem definitions. CODE_FUNC is a narrative description of a programming function and its behavior.
3. **Execution**: TASK does not execute any actions; it merely specifies what should be done. CODE_FUNC describes a sequence of actions that are executed in a simulation.
4. **Environment Setup**: TASK includes a domain and problem definition, while CODE_FUNC mentions setting up the environment and resetting the task state, which is not present in TASK.
5. **Feedback Mechanism**: TASK does not provide feedback or completion checks; it simply states the action. CODE_FUNC includes checks for task completion and prints messages based on the execution state.
6. **Recording**: CODE_FUNC mentions the option to record video of the task execution, which is not relevant in TASK.
7. **Agent Interaction**: TASK focuses on the robot's actions (picking and placing), while CODE_FUNC elaborates on the agent's interaction with the environment and the sequence of actions taken.
8. **Cleanup**: CODE_FUNC includes a shutdown procedure for the environment after task execution, which is not addressed in TASK. 

In summary, the two serve different purposes and are structured differently, with CODE_FUNC providing a more detailed operational context for the actions described in 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
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        item3_pos = positions['item3']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        for item_pos in [item1_pos, item2_pos, item3_pos]:
            print(f"[Task] Picking item from the table.")
            obs, reward, done = pick(
                env,
                task,
                target_pos=item_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 an item!")
                return

            print(f"[Task] Placing item 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)
                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 items. 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 three items and a bin. The task consists of a series of steps where the program picks each item from a table and places it into the bin, checking after each action if the task is complete. If an error occurs during the execution of the task, it is caught and reported. Finally, the environment is shut down properly after the task is completed or if an error occurs. The function is executed when the script is run directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a planning domain and problem for a robot to dispose of items, 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, predicates, and problem definitions. CODE_FUNC is written in a programming language (likely Python), focusing on procedural steps and error handling.
3. **Execution**: TASK does not execute any actions; it merely defines what actions can be taken and under what conditions. CODE_FUNC actually runs a simulation of the defined actions.
4. **Error Handling**: TASK does not include any error handling mechanisms, while CODE_FUNC explicitly mentions catching and reporting errors during execution.
5. **Environment Setup**: TASK specifies the initial state of the environment in terms of predicates, while CODE_FUNC includes steps for setting up the environment and initializing video recording capabilities.
6. **Completion Check**: TASK does not include any mechanism for checking if the task is complete; CODE_FUNC includes checks after each action to determine if the task has been completed.
7. **Shutdown Procedure**: TASK does not mention any shutdown procedures, whereas CODE_FUNC includes a proper shutdown of the environment after task completion or error occurrence.

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
        item1_pos = positions['item1']
        item2_pos = positions['item2']
        item3_pos = positions['item3']
        bin_pos = positions['bin']

        # === Execute Task Plan ===
        try:
            # Step 1: Pick item1
            print("[Task] Picking item1 from 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 in bin
            print("[Task] Placing item1 in bin.")
            obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
            if done:
                print("[Task] Task ended after placing item1!")
                return

            # Step 3: Pick item2
            print("[Task] Picking item2 from 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 bin
            print("[Task] Placing item2 in 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 item3
            print("[Task] Picking item3 from table.")
            obs, reward, done = pick(env, task, target_pos=item3_pos, approach_distance=0.15)
            if done:
                print("[Task] Task ended after picking item3!")
                return

            # Step 6: Place item3 in bin
            print("[Task] Placing item3 in 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).")

        except Exception as error:
            print(f"[Task] An error occurred: {error}")

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

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

