=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving a robotic gripper interacting with a bottom drawer. 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 function retrieves the positions of relevant objects and defines specific target positions for the gripper's actions.

The task consists of several key steps: first, the gripper rotates to a specified orientation. Next, it moves to the side position of the bottom drawer, followed by moving to the anchor position of the drawer. The gripper then picks up the bottom drawer and finally pulls it to open it. After each action, the function checks if the task has completed successfully or if it ended prematurely. Regardless of the outcome, the environment is shut down at the end of the task. The function prints status messages throughout to indicate progress and completion.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Nature of Content**: TASK is a formal representation of a planning domain and problem in PDDL, while CODE_FUNC is a descriptive summary of a function that implements a simulation task.
2. **Purpose**: TASK defines the rules and actions for a robotic system to interact with drawers, whereas CODE_FUNC describes the execution of a specific instance of that task.
3. **Structure**: TASK is structured in a formal syntax with predicates, actions, and parameters, while CODE_FUNC is written in natural language and outlines procedural steps.
4. **Level of Detail**: TASK provides detailed preconditions and effects for each action, while CODE_FUNC gives a high-level overview of the steps without specifying preconditions or effects.
5. **Context**: TASK is context-free and focuses solely on the domain of drawer manipulation, while CODE_FUNC implies a specific scenario involving a bottom drawer and includes additional context about simulation and environment setup.
6. **Execution vs. Definition**: TASK defines what can be done in the domain (the actions and their conditions), while CODE_FUNC describes how to execute a specific sequence of those actions in a simulation.
7. **Output**: TASK does not specify any output or feedback mechanism, while CODE_FUNC mentions status messages indicating progress and completion of the task.

Refined Code Generated:
-------------------
import numpy as np
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 =====")
    
    env, task = setup_environment()
    try:
        obs = task.reset()[1]
        init_video_writers(obs)

        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)

        positions = get_object_positions()
        bottom_side_pos = positions['bottom_side_pos']
        bottom_anchor_pos = positions['bottom_anchor_pos']

        print("[Task] Rotating gripper to 90 degrees.")
        obs, _, done = rotate(env, task, target_quat=np.array([0, 0, 1, 0]), max_steps=100, threshold=0.05, timeout=10.0)
        if done:
            print("[Task] Task ended during rotation!")
            return

        print("[Task] Moving to side position of the bottom drawer.")
        obs, _, done = move(env, task, target_pos=bottom_side_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during move to side position!")
            return

        print("[Task] Moving to anchor position of the bottom drawer.")
        obs, _, done = move(env, task, target_pos=bottom_anchor_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during move to anchor position!")
            return

        print("[Task] Picking the bottom drawer.")
        obs, _, done = pick(env, task, target_pos=bottom_anchor_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended during pick!")
            return

        print("[Task] Pulling the bottom drawer to open it.")
        obs, _, done = pull(env, task, pull_distance=0.1, pull_axis='y', max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during pull!")
            return

        print("[Task] Successfully opened the bottom drawer!")

    finally:
        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 a robotic gripper interacting with a drawer. It begins by setting up the environment and resetting the task to its initial state. The function initializes video recording capabilities and retrieves the positions of various objects in the environment.

The task consists of several key steps: first, the gripper is rotated to a specific orientation. Next, the gripper moves to the side position of the middle drawer, followed by moving to the anchor position of the same drawer. The gripper then attempts to pick up the middle drawer and finally pulls it open. After each action, the function checks if the task is completed and prints appropriate messages. 

Regardless of the outcome, the environment is ensured to be properly shut down at 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 describes a specific instruction for an action (pulling open a drawer), while CODE_FUNC outlines a function that simulates a series of actions involving a robotic gripper interacting with a drawer.
2. **Detail Level**: TASK is a high-level instruction without implementation details, whereas CODE_FUNC provides a detailed sequence of actions and the context in which they occur.
3. **Execution Context**: TASK is a directive meant to be interpreted by a planner or agent, while CODE_FUNC is a concrete implementation that runs in a programming environment.
4. **Action Sequence**: TASK focuses solely on the action of pulling open a drawer, while CODE_FUNC includes multiple steps (rotation, movement, picking, and pulling) leading up to the action of pulling the drawer open.
5. **Environment Setup**: TASK does not mention any setup or initialization, while CODE_FUNC includes environment setup, such as initializing video recording and resetting the task state.
6. **Feedback Mechanism**: TASK does not specify any feedback or outcome reporting, while CODE_FUNC includes checks for task completion and prints messages based on the outcome.
7. **Termination**: TASK does not address what happens after the action is performed, whereas CODE_FUNC ensures proper shutdown of the environment after the task is completed.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and contain different levels of detail and context.

Refined Code Generated:
-------------------
import numpy as np
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
        _, 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 the positions for the drawers
        side_pos_middle = positions['middle_side_pos']
        middle_anchor_pos = positions['middle_anchor_pos']

        # === Execute the Plan ===
        # Step 1: Rotate the gripper to the correct orientation
        print("[Task] Rotating gripper to ninety degrees.")
        obs, reward, done = rotate(env, task, target_quat=np.array([0, 0, 1, 0]), max_steps=100, threshold=0.05, timeout=10.0)
        if done:
            print("[Task] Task ended after rotating!")
            return

        # Step 2: Move to the side position of the middle drawer
        print("[Task] Moving to side position of the middle drawer.")
        obs, reward, done = move(env, task, target_pos=side_pos_middle, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended after moving to side position!")
            return

        # Step 3: Move to the anchor position of the middle drawer
        print("[Task] Moving to anchor position of the middle drawer.")
        obs, reward, done = move(env, task, target_pos=middle_anchor_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended after moving to anchor position!")
            return

        # Step 4: Pick the middle drawer
        print("[Task] Picking the middle drawer.")
        obs, reward, done = pick(env, task, target_pos=middle_anchor_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!")
            return

        # Step 5: Pull the middle drawer open
        print("[Task] Pulling the middle drawer open.")
        obs, reward, done = pull(env, task, pull_distance=0.1, pull_axis='x', max_steps=100, threshold=0.01, 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 3 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving a robotic gripper interacting with a drawer. It begins by setting up the environment and resetting the task to its initial state. The function initializes video recording capabilities and retrieves the positions of various objects, specifically the positions of the drawers.

The task consists of a series of steps: first, the gripper is rotated to a specific orientation. Next, the gripper moves to the side position of the bottom drawer, followed by moving to the anchor position of the same drawer. The gripper then attempts to pick up the bottom drawer and finally pulls it open. After each step, the function checks if the task is completed and prints appropriate messages based on the outcome.

Regardless of the task's success or failure, the environment is ensured to be properly shut down at the end of the function. The overall behavior is to simulate the process of opening a drawer using a robotic gripper while capturing observations and potentially recording the simulation.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK defines a domain and problem for a planning system to open a drawer using a robotic gripper, focusing on the actions and their preconditions/effects.
   - CODE_FUNC describes a function that simulates the task of opening a drawer, detailing the procedural steps and environment management.

2. **Structure**: 
   - TASK is structured in PDDL (Planning Domain Definition Language) format, which is used for automated planning and scheduling.
   - CODE_FUNC is written in a programming language (likely Python or similar), focusing on procedural execution rather than declarative definitions.

3. **Execution**: 
   - TASK outlines a theoretical framework for planning actions based on defined predicates and actions.
   - CODE_FUNC executes a sequence of actions in a simulation environment, including video recording and state management.

4. **State Management**: 
   - TASK defines the initial state and the effects of actions in a logical manner, focusing on the relationships between objects.
   - CODE_FUNC manages the state through procedural code, checking conditions and printing messages based on the success or failure of actions.

5. **Output**: 
   - TASK does not produce output in the form of messages or results; it is concerned with the logical representation of actions.
   - CODE_FUNC provides feedback through printed messages indicating the status of the task and whether it was completed successfully.

6. **Focus on Simulation**: 
   - TASK is purely about the planning aspect without any simulation or execution context.
   - CODE_FUNC explicitly mentions simulation, video recording, and the management of the environment, indicating a focus on practical execution.

7. **Error Handling**: 
   - TASK does not address error handling or task completion checks.
   - CODE_FUNC includes checks for task completion and handles the environment shutdown regardless of the outcome.

8. **Level of Abstraction**: 
   - TASK operates at a higher level of abstraction, focusing on the logical relationships and requirements for planning.
   - CODE_FUNC operates at a lower level, detailing specific steps and interactions in a simulation context.

9. **Interactivity**: 
   - TASK is static and does not involve interactive elements.
   - CODE_FUNC implies interactivity through the simulation of actions and responses based on the state of the environment.

10. **Implementation Detail**: 
    - TASK does not specify how actions are implemented or executed.
    - CODE_FUNC provides a detailed sequence of operations that are executed in a specific order to achieve the task.

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

Refined Code Generated:
-------------------
import numpy as np
from env import setup_environment, shutdown_environment
from skill_code import rotate, move, pick, pull
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 the positions for the drawers
        bottom_anchor_pos = positions['bottom_anchor_pos']

        # === Example Task Plan ===
        # Step 1: Rotate the gripper to the correct orientation
        print("[Task] Rotating gripper to ninety degrees.")
        obs, _, done = rotate(env, task, target_quat=np.array([0, 0, 1, 0]), max_steps=100, threshold=0.05, timeout=10.0)
        if done:
            print("[Task] Task ended after rotating the gripper!")
            return

        # Step 2: Move to the side position of the bottom drawer
        print("[Task] Moving to side position of the bottom drawer.")
        obs, _, done = move(env, task, target_pos=bottom_anchor_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended after moving to side position!")
            return

        # Step 3: Move to the anchor position of the bottom drawer
        print("[Task] Moving to anchor position of the bottom drawer.")
        obs, _, done = move(env, task, target_pos=bottom_anchor_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended after moving to anchor position!")
            return

        # Step 4: Pick the bottom drawer
        print("[Task] Picking the bottom drawer.")
        obs, _, done = pick(env, task, target_pos=bottom_anchor_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 the drawer!")
            return

        # Step 5: Pull the bottom drawer to open it
        print("[Task] Pulling the bottom drawer to open it.")
        obs, _, done = pull(env, task, pull_distance=0.1, pull_axis='y', max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task completed successfully! Drawer is now open.")
        else:
            print("[Task] Task not completed yet (done=False).")

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

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

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

=== Instruction 4 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving a robotic gripper interacting with a cabinet drawer. It begins by setting up the environment and resetting the task to its initial state. The function initializes video recording capabilities to capture the simulation. It retrieves the positions of various objects, specifically the anchors for the drawers.

The task consists of several key steps: first, the gripper is rotated to a specified orientation. Next, the gripper moves to the side position of the middle drawer, followed by moving to the anchor position of the same drawer. The gripper then attempts to pick up the middle drawer and finally pulls it to unlock the cabinet. After each action, the function checks if the task is completed and prints appropriate messages based on the outcome. Regardless of the task's success, the environment is shut down at the end of the function.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction to unlock a cabinet by opening a drawer.
   - CODE_FUNC outlines a function that simulates the execution of the TASK, detailing the steps taken by a robotic gripper.

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

3. **Components**:
   - TASK includes PDDL domain and problem definitions, focusing on the actions and predicates involved in the task.
   - CODE_FUNC includes procedural steps, environment setup, and video recording capabilities, which are not present in TASK.

4. **Execution**:
   - TASK does not specify how the actions are executed or in what order.
   - CODE_FUNC explicitly describes the sequence of actions performed by the gripper, including rotation, movement, picking, and pulling.

5. **Feedback Mechanism**:
   - TASK does not include any feedback or outcome assessment.
   - CODE_FUNC includes checks for task completion and prints messages based on the outcome of the actions.

6. **Environment Management**:
   - TASK does not mention any management of the simulation environment.
   - CODE_FUNC includes steps to initialize and shut down the environment, which are absent in TASK.

7. **Recording**:
   - TASK does not mention any form of recording or monitoring.
   - CODE_FUNC specifies the initialization of video recording capabilities to capture the simulation.

8. **Object Interaction**:
   - TASK focuses on the interaction with the drawer in a general sense.
   - CODE_FUNC details the specific interactions with the middle drawer, including its positions and states.

9. **State Initialization**:
   - TASK initializes the state of the objects in the PDDL format.
   - CODE_FUNC describes resetting the task to its initial state in a procedural manner.

10. **Outcome Handling**:
    - TASK does not address how to handle the outcome of the actions.
    - CODE_FUNC includes a mechanism to check if the task is completed and respond accordingly.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as TASK provides a high-level description of the goal, while CODE_FUNC details the implementation and execution of that goal in a simulation context.

Refined Code Generated:
-------------------
import numpy as np
from env import setup_environment, shutdown_environment
from skill_code import rotate, move, pick, pull
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
        task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(task.get_observation())

        # 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 the positions for the drawers
        middle_anchor_pos = positions['middle_anchor_pos']

        # === Execute the Plan ===
        # Step 1: Rotate the gripper to the correct orientation
        obs, reward, done = rotate(env, task, target_quat=np.array([0, 0, 0, 1]))  # Assuming zero rotation
        if done:
            print("[Task] Task ended after rotation!")
            return

        # Step 2: Move to the anchor position of the middle drawer
        obs, reward, done = move(env, task, target_pos=middle_anchor_pos)
        if done:
            print("[Task] Task ended after moving to anchor position!")
            return

        # Step 3: Pick the middle drawer
        obs, reward, done = pick(env, task, target_pos=middle_anchor_pos)
        if done:
            print("[Task] Task ended after picking the drawer!")
            return

        # Step 4: Pull the middle drawer to unlock the cabinet
        obs, reward, done = pull(env, task, pull_distance=0.1)  # Assuming a pull distance of 0.1
        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 a robotic gripper interacting with a drawer. 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 objects in the environment, specifically the positions for the middle drawer.

The task consists of several key steps: first, the gripper is rotated to a specific orientation. Next, the gripper moves to the side position of the middle drawer, followed by moving to the anchor position of the drawer. The gripper then attempts to pick up the middle drawer and finally pulls the drawer to open it. After each action, the function checks if the task has ended prematurely. Regardless of the outcome, the environment is shut down at the end of the task. The function is executed when the script is run directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Nature of Content**: TASK is a formal representation of a planning problem in PDDL (Planning Domain Definition Language), while CODE_FUNC is a description of a function that implements a simulation task in a programming context.
2. **Purpose**: TASK defines the actions, preconditions, and effects for a robotic gripper interacting with drawers, focusing on the planning aspect. CODE_FUNC describes the procedural steps taken to execute the task in a simulation environment.
3. **Structure**: TASK is structured in a domain and problem format with predicates and actions, while CODE_FUNC is written in a narrative format explaining the sequence of operations.
4. **Level of Abstraction**: TASK operates at a higher level of abstraction, focusing on the logical relationships and requirements for actions, whereas CODE_FUNC provides a lower-level, step-by-step procedural description of how to perform the task.
5. **Execution Context**: TASK is designed for a planning system to generate a sequence of actions, while CODE_FUNC is intended for execution in a programming environment, likely involving a simulation framework.
6. **Detailing of Actions**: TASK specifies actions with parameters, preconditions, and effects, while CODE_FUNC summarizes the actions without detailing the underlying logic or conditions for each step.
7. **Outcome Handling**: TASK does not explicitly mention handling outcomes or errors, while CODE_FUNC includes a check for premature task termination and ensures the environment is shut down at the end of the task.

In summary, the two representations serve different purposes and contexts, with TASK focusing on planning and CODE_FUNC on execution.

Refined Code Generated:
-------------------
import numpy as np
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
        task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(task.get_observation())

        # 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 the positions for the drawers
        middle_anchor_pos = positions['middle_anchor_pos']
        side_pos_middle = positions['middle_side_pos']

        # === Execute the Plan ===
        # Step 1: Rotate the gripper to the correct orientation
        print("[Task] Rotating gripper to ninety degrees.")
        obs, _, done = rotate(env, task, target_quat=np.array([0, 0, 1, 0]), max_steps=100, threshold=0.05, timeout=10.0)
        if done:
            print("[Task] Task ended during rotation!")
            return

        # Step 2: Move to the side position of the middle drawer
        print("[Task] Moving to side position of the middle drawer.")
        obs, _, done = move(env, task, target_pos=side_pos_middle, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during movement!")
            return

        # Step 3: Move to the anchor position of the middle drawer
        print("[Task] Moving to anchor position of the middle drawer.")
        obs, _, done = move(env, task, target_pos=middle_anchor_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during movement!")
            return

        # Step 4: Pick the middle drawer
        print("[Task] Picking the middle drawer.")
        obs, _, done = pick(env, task, target_pos=middle_anchor_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task ended during picking!")
            return

        # Step 5: Pull the middle drawer to open it
        print("[Task] Pulling the middle drawer to open it.")
        obs, _, done = pull(env, task, pull_distance=0.2, pull_axis='y', max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended during pulling!")
            return

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

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

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

