=== 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 item. It begins by setting up the environment and resetting the task to its initial state. It optionally initializes video recording for the simulation. The function retrieves the positions of specific objects, namely an item and a bin. It then attempts to pick the item from a designated position and checks if the task is completed. If successful, it proceeds to place the item in the bin, again checking for task completion. Finally, the environment is shut down properly, and the function concludes with a message indicating the end of the task.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a domain and problem for a robot disposal scenario, while CODE_FUNC describes a function for simulating the task of picking and placing an item.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, defining actions, predicates, and initial conditions, whereas CODE_FUNC is a narrative description of a procedural function.
3. **Detail Level**: TASK includes specific actions, preconditions, and effects related to robot operations, while CODE_FUNC provides a high-level overview without detailing the underlying logic or conditions.
4. **Focus**: TASK focuses on the formal representation of a planning problem, including the environment and actions, while CODE_FUNC emphasizes the execution of a simulation task.
5. **Language**: TASK uses a declarative language (PDDL) for defining planning problems, while CODE_FUNC uses a descriptive narrative format to explain the function's purpose and flow.
6. **Execution**: TASK does not describe execution or runtime behavior, while CODE_FUNC implies a sequence of operations that occur during the simulation.
7. **Environment Setup**: TASK specifies the initial state of the environment in terms of predicates, while CODE_FUNC mentions setting up the environment but does not detail the initial state in a formal way.
8. **Outcome Reporting**: TASK does not include any reporting of outcomes or task completion, while CODE_FUNC mentions checking for task completion and providing a concluding message.

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']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        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

        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 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 needed for the task. 

The task consists of two main steps: first, it picks up an item from a table, and if successful, it then places that item into a designated bin. After each action, it checks if the task is complete. Finally, the environment is shut down to ensure proper cleanup. The function prints status messages throughout the process to indicate progress and completion.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a domain and problem for a robot disposal scenario using PDDL, while CODE_FUNC describes a function for running a simulation task involving object manipulation.
2. **Structure**: TASK is structured in a formal PDDL format with defined actions, predicates, and problem setup, whereas CODE_FUNC is a narrative description of a procedural function.
3. **Action Definition**: TASK explicitly defines actions (pick, place, close_gripper, press) with preconditions and effects, while CODE_FUNC does not define actions but describes the sequence of operations in a simulation.
4. **Environment Setup**: TASK includes a detailed setup of the environment using PDDL syntax, including object types and initial conditions, while CODE_FUNC describes the setup in a more general and less formal manner.
5. **Simulation vs. Planning**: TASK is focused on planning and reasoning about actions in a defined domain, while CODE_FUNC emphasizes executing a simulation of those actions.
6. **Feedback Mechanism**: TASK does not mention feedback or status updates during execution, while CODE_FUNC explicitly states that it prints status messages to indicate progress and completion.
7. **Completion Check**: TASK does not specify how to determine if the task is complete, while CODE_FUNC includes a step to check for task completion after each action.
8. **Video Recording**: CODE_FUNC mentions the optional initialization of video recording for the simulation, which is not present in TASK. 

Overall, the two serve different purposes and are structured differently, reflecting their distinct roles in task definition and 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
        item1_pos = positions['item1']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        # Step 1: Pick item1 from the table
        print("[Task] Picking item1 from the table.")
        obs, reward, done = pick(env, task, 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, 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 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 specific objects, including two items and a bin. 

The task consists of a series of steps: first, it picks up the first item and places it in the bin, then picks up a second item and places it in the bin as well. 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 describes a specific instruction for a robot to perform (picking up rubbish and placing it in a trash can).
   - CODE_FUNC outlines a function that simulates the execution of a series of actions related to picking and placing objects.

2. **Structure**: 
   - TASK is presented in a formal PDDL (Planning Domain Definition Language) format, defining actions, preconditions, and effects.
   - CODE_FUNC is described in a narrative format, explaining the function's purpose and steps without formal syntax.

3. **Detail Level**: 
   - TASK includes detailed definitions of actions, preconditions, and effects, which are essential for planning and execution in a robotic context.
   - CODE_FUNC provides a high-level overview of the simulation process without delving into the specifics of action definitions or preconditions.

4. **Execution Context**: 
   - TASK is designed for a planning system that interprets PDDL to execute actions based on defined conditions.
   - CODE_FUNC is intended for a programming environment where a function is executed to simulate the task.

5. **Focus on Environment**: 
   - TASK includes environmental conditions (e.g., dark room affecting actions) as part of its preconditions.
   - CODE_FUNC mentions setting up the environment but does not detail how environmental conditions affect the actions taken.

6. **Output and Feedback**: 
   - TASK does not specify any output or feedback mechanism; it focuses solely on the actions and their effects.
   - CODE_FUNC includes a mechanism for checking task completion and printing messages, indicating a feedback loop.

7. **Initialization**: 
   - TASK does not include any initialization steps; it assumes the environment is already set up.
   - CODE_FUNC explicitly mentions resetting the task to its initial state and optionally initializing video recording.

8. **Action Sequence**: 
   - TASK describes a single action (picking up rubbish) without detailing the sequence of actions.
   - CODE_FUNC describes a sequence of actions (picking and placing multiple items) as part of the simulation.

9. **Directness of Instruction**: 
   - TASK is a direct command to a robot.
   - CODE_FUNC is a description of a function that implements a series of commands.

10. **Language**: 
    - TASK uses formal PDDL language.
    - CODE_FUNC uses natural language to describe the function's behavior.

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

        # === Task Plan Execution ===
        # Step 1: Pick up item1
        print("[Task] Picking up item1 at:", item1_pos)
        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 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

        # Step 3: Pick up tomato2
        print("[Task] Picking up 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 4: Place tomato2 in the bin
        print("[Task] Placing tomato2 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 that simulates a task in a virtual environment where an agent performs a series of actions to pick and place items into a bin. 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 function retrieves the positions of various objects in the environment, including two items and a bin. It then follows a predefined sequence of steps: picking up the first item from the table and placing it into the bin, followed by picking up a second item and placing it into the bin, and finally picking up a third item and placing it into the bin. After each action, it checks if the task is complete and prints a message if it is. 

At the end of the task, it ensures that the environment is properly shut down. 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 follow (throwing away trash), while CODE_FUNC outlines a function that simulates a series of actions in a virtual environment.

2. **Level of Detail**: 
   - TASK provides a high-level instruction without implementation details, whereas CODE_FUNC includes specific steps and sequences for executing the task.

3. **Environment Setup**: 
   - TASK does not mention any setup or initialization of the environment, while CODE_FUNC explicitly states that it sets up the environment and resets the task to its initial state.

4. **Action Sequence**: 
   - TASK implies a single action (throwing away trash), while CODE_FUNC details multiple actions (picking and placing multiple items into a bin).

5. **Completion Check**: 
   - TASK does not include any mechanism for checking if the task is complete, whereas CODE_FUNC includes checks to determine if the task has been completed after each action.

6. **Output**: 
   - TASK does not specify any output or feedback mechanism, while CODE_FUNC mentions printing a message upon task completion.

7. **Execution Context**: 
   - TASK is a standalone instruction, while CODE_FUNC is designed to be executed within a script as the main program.

8. **Recording Capability**: 
   - TASK does not mention any recording of actions, while CODE_FUNC includes an option to record video of the task execution.

9. **Object Interaction**: 
   - TASK focuses on the action of throwing away trash, while CODE_FUNC involves multiple interactions with various objects (items and a bin).

10. **Preconditions and Effects**: 
    - TASK does not specify preconditions or effects of actions, while CODE_FUNC implies a structured approach with defined actions and their consequences.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as TASK is a high-level instruction while CODE_FUNC provides a detailed implementation of a series of actions in a simulated 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
        item1_pos = positions['item1']
        tomato2_pos = positions['tomato2']
        item2_pos = positions['item2']
        bin_pos = positions['bin']

        # === Task Plan Execution ===
        # Step 1: Pick item1 from the table
        print("[Task] Picking item1 from the table.")
        obs, _, done = pick(env, task, item1_pos)
        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, _, done = place(env, task, bin_pos)
        if done:
            print("[Task] Task ended after placing item1!")
            return

        # Step 3: Pick tomato2 from the table
        print("[Task] Picking tomato2 from the table.")
        obs, _, done = pick(env, task, tomato2_pos)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 4: Place tomato2 into the bin
        print("[Task] Placing tomato2 into the bin.")
        obs, _, done = place(env, task, bin_pos)
        if done:
            print("[Task] Task ended after placing tomato2!")
            return

        # Step 5: Pick item2 from the table
        print("[Task] Picking item2 from the table.")
        obs, _, done = pick(env, task, item2_pos)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 6: Place item2 into the bin
        print("[Task] Placing item2 into the bin.")
        obs, _, done = place(env, task, bin_pos)
        if done:
            print("[Task] Task ended after placing item2!")
            return

        print("[Task] All trash has been disposed of successfully!")

    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 that simulates a task in a robotic environment. 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 needed for the task.

The task consists of a series of steps where the robot picks up items from a table and places them into a bin. Specifically, it picks "item1," places it in the bin, picks "tomato2," places it in the bin, picks "item2," and finally places it in the bin. After each action, the function checks if the task has ended and prints a message accordingly. If all items are successfully placed in the bin, it indicates that the task is completed successfully.

Finally, the environment is shut down to ensure proper cleanup 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 dispose of rubbish on a table.
   - CODE_FUNC outlines a function that simulates a series of actions for a robot to pick and place items in a bin.

2. **Structure**: 
   - TASK is presented in a PDDL format defining a domain and problem.
   - CODE_FUNC is written in a programming language (likely Python) and describes a procedural function.

3. **Detail Level**: 
   - TASK includes detailed preconditions and effects for actions in a formalized manner.
   - CODE_FUNC provides a high-level overview of the task without formal preconditions or effects.

4. **Action Specification**: 
   - TASK specifies actions like `pick`, `place`, `close_gripper`, and `press` with their respective parameters and conditions.
   - CODE_FUNC describes a sequence of actions but does not define them formally or specify conditions.

5. **Environment Setup**: 
   - TASK includes a detailed description of the initial state of the environment using predicates.
   - CODE_FUNC mentions setting up the environment but does not detail the initial state in a formal way.

6. **Execution Flow**: 
   - TASK does not specify the order of actions beyond the defined actions.
   - CODE_FUNC explicitly describes the order of actions taken by the robot in a sequential manner.

7. **Completion Criteria**: 
   - TASK does not define what constitutes the completion of the task.
   - CODE_FUNC includes a check for task completion and prints a message accordingly.

8. **Error Handling**: 
   - TASK does not address error handling or conditions under which actions cannot be performed.
   - CODE_FUNC implies a check for successful completion but does not detail error handling.

9. **Output**: 
   - TASK does not produce output or feedback.
   - CODE_FUNC includes print statements to indicate the status of the task.

10. **Context**: 
    - TASK is focused on the logical representation of actions in a robotic domain.
    - CODE_FUNC is focused on the practical implementation of those actions in a simulation environment.

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

        # === Execute the Plan ===
        # Step 1: Pick item1 from the table
        obs, reward, done = pick(env, task, item1_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item1!")
            return

        # Step 2: Place item1 in the bin
        obs, reward, done = place(env, task, bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing item1!")
            return

        # Step 3: Pick tomato2 from the table
        obs, reward, done = pick(env, task, tomato2_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking tomato2!")
            return

        # Step 4: Place tomato2 in the bin
        obs, reward, done = place(env, task, bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after placing tomato2!")
            return

        # Step 5: Pick item2 from the table
        obs, reward, done = pick(env, task, item2_pos, approach_distance=0.15)
        if done:
            print("[Task] Task ended after picking item2!")
            return

        # Step 6: Place item2 in the bin
        obs, reward, done = place(env, task, 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 =====")
-------------------

