=== 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 drawers. 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 drawer components and then executes a series of steps: rotating the gripper, moving to the side and anchor positions of the bottom drawer, picking up the drawer, and finally pulling it open. After each step, it checks if the task is completed and prints appropriate messages. The environment is ensured to shut down properly at the end of the task.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a domain and problem for a planning system, while CODE_FUNC describes a function for simulating a 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 types, predicates, and actions, while CODE_FUNC focuses on the procedural steps to execute the task.
4. **Execution**: TASK outlines the preconditions and effects of actions in a planning context, while CODE_FUNC describes the sequential execution of actions in a simulation.
5. **Environment Setup**: TASK specifies the initial state and conditions for a planning problem, while CODE_FUNC mentions setting up the environment and resetting the task state for simulation.
6. **Output**: TASK does not specify output messages, while CODE_FUNC includes printing messages to indicate task completion.
7. **Recording**: CODE_FUNC has an optional feature for video recording, which is not present in TASK.
8. **Shutdown Procedure**: CODE_FUNC includes a shutdown procedure for the environment, which is not mentioned in 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 =====")
    
    # === 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 positions for the drawers
        bottom_anchor_pos = positions['bottom_anchor_pos']
        bottom_side_pos = positions['bottom_side_pos']

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

        # Step 2: Move to the side position of the bottom drawer
        obs, _, done = move(env, task, target_pos=bottom_side_pos)
        if done:
            print("[Task] Task ended after moving to side position!")
            return

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

        # Step 4: Pick the bottom drawer
        obs, _, done = pick(env, task, target_pos=bottom_anchor_pos)
        if done:
            print("[Task] Task ended after picking the drawer!")
            return

        # Step 5: Pull the bottom drawer to open it
        obs, _, done = pull(env, task, pull_distance=0.1)  
        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 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 drawers. 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 various objects, specifically the positions of the drawers.

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 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 out a specified distance. After each step, the function checks if the task is completed and prints appropriate messages. The environment is ensured to be shut down properly at the end of the task.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Nature of Content**: TASK is a formal representation of a planning problem in PDDL, while CODE_FUNC is a description of a procedural function for simulating a task.
2. **Format**: TASK is structured in a domain-specific language (PDDL), whereas CODE_FUNC is written in natural language.
3. **Purpose**: TASK defines the rules and actions for a planning problem, while CODE_FUNC describes the implementation of a simulation task.
4. **Detail Level**: TASK includes specific predicates, actions, and preconditions, while CODE_FUNC provides a high-level overview of the steps involved in the simulation without detailing the underlying logic or conditions.
5. **Execution**: TASK is meant to be executed by a planner that interprets PDDL, while CODE_FUNC is intended to be executed as a program or script in a programming environment.
6. **Focus**: TASK focuses on the logical structure and requirements of the task, while CODE_FUNC emphasizes the procedural steps and flow of execution in a simulation context.
7. **State Representation**: TASK explicitly defines the initial state and conditions for actions, whereas CODE_FUNC describes the task in terms of actions taken without detailing the state transitions.
8. **Outcome Specification**: TASK specifies effects of actions in terms of predicates, while CODE_FUNC describes outcomes in a narrative form without formal predicates.

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
        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 positions for the drawers
        bottom_anchor_pos = positions['bottom_anchor_pos']
        bottom_side_pos = positions['bottom_side_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 bottom drawer
        print("[Task] Moving to side position of the bottom drawer.")
        obs, reward, done = move(env, task, target_pos=bottom_side_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, reward, 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, reward, 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!")
            return

        # Step 5: Pull the bottom drawer
        print("[Task] Pulling the bottom drawer.")
        obs, reward, 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! 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 a robotic gripper interacting with a 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 positions of relevant objects are retrieved, specifically for the bottom 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 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 code 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. **Purpose**: TASK defines a planning domain and problem for opening a drawer using PDDL, while CODE_FUNC describes a function that simulates the execution of the task in a programming context.
2. **Format**: TASK is structured in PDDL format, which is used for defining planning problems, whereas CODE_FUNC is written in a programming language (likely Python or similar) and describes procedural steps.
3. **Components**: TASK includes definitions of actions, predicates, and types, while CODE_FUNC focuses on the sequence of operations to be performed during the simulation.
4. **Execution**: TASK does not execute any actions; it merely defines them, while CODE_FUNC actually runs a simulation of the defined actions.
5. **Environment Setup**: TASK specifies the initial state and conditions for the planning problem, whereas CODE_FUNC includes steps for setting up the environment and resetting the task.
6. **Recording**: CODE_FUNC mentions optional video recording of the simulation, which is not present in TASK.
7. **Action Checking**: CODE_FUNC includes checks for premature task termination after each action, which is not a feature of TASK.
8. **Finalization**: CODE_FUNC explicitly states that the environment is shut down at the end of the task, while TASK does not include any finalization steps.

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():
    print("===== Starting Skeleton Task =====")
    env, task = setup_environment()
    try:
        descriptions, obs = task.reset()
        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, 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 during rotation!")
            return
        print("[Task] Moving to side position of the bottom drawer.")
        obs, reward, 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, reward, 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, reward, 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, reward, 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
    finally:
        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 drawers. It begins by setting up the environment and resetting the task to its initial state. It initializes video recording capabilities to capture the simulation. The function retrieves the positions of various drawer components and then executes a series of steps to manipulate the bottom drawer. 

First, the gripper rotates to a specified angle. Next, it 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 drawer and finally pulls it to unlock it. Throughout the process, the function checks if each step is completed successfully and prints messages indicating the status of the task. After all operations, it ensures 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 defines a specific problem in a planning domain, while CODE_FUNC describes a function that implements a simulation of that task.
2. **Format**: TASK is structured in PDDL (Planning Domain Definition Language), whereas CODE_FUNC is written in a programming language (likely Python or similar).
3. **Components**: TASK includes definitions of actions, predicates, and types, while CODE_FUNC focuses on procedural steps and function execution.
4. **Execution**: TASK outlines a theoretical framework for planning and reasoning about actions, while CODE_FUNC describes the actual execution of those actions in a simulation.
5. **State Representation**: TASK uses predicates to represent the state of the world, while CODE_FUNC uses variables and function calls to manage state during execution.
6. **Feedback Mechanism**: TASK does not provide feedback on the execution of actions, while CODE_FUNC includes print statements to indicate the status of each step.
7. **Environment Setup**: TASK specifies the initial conditions and requirements for the planning problem, while CODE_FUNC includes setup for video recording and environment management.
8. **Action Sequence**: TASK defines a sequence of actions in a logical manner, while CODE_FUNC describes the execution of those actions in a procedural format.
9. **Error Handling**: TASK does not address error handling, while CODE_FUNC implies checking for successful completion of each step.
10. **Execution Context**: TASK is domain-specific and abstract, while CODE_FUNC is context-specific and concrete, designed to run in a specific programming environment.

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():
    print("===== Starting Skeleton Task =====")
    
    env, task = setup_environment()
    try:
        descriptions, obs = task.reset()
        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_anchor_pos = positions['bottom_anchor_pos']
        bottom_side_pos = positions['bottom_side_pos']

        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

        obs, reward, done = move(env, task, target_pos=bottom_side_pos, max_steps=100, threshold=0.01, timeout=10.0)
        if done:
            print("[Task] Task ended after moving to side position!")
            return

        obs, reward, 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

        obs, reward, 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

        obs, reward, 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 unlocked.")
        else:
            print("[Task] Task not completed yet (done=False).")

    finally:
        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 drawers. 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 various objects, specifically the drawers.

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 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 relevant messages. At the end of the task, the environment is properly shut down. The overall behavior is to simulate the process of opening a drawer with a robotic gripper.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robotic gripper to perform (slide open any available drawer).
   - CODE_FUNC outlines a function that simulates the execution of a series of actions to achieve the task of opening a drawer.

2. **Structure**: 
   - TASK is presented in a formal PDDL (Planning Domain Definition Language) format, defining actions, preconditions, and effects.
   - CODE_FUNC is a narrative description of a function that implements the task, detailing the steps taken in a simulation.

3. **Detail Level**: 
   - TASK includes detailed definitions of actions, their parameters, preconditions, and effects, which are essential for automated planning.
   - CODE_FUNC provides a high-level overview of the simulation process without the formal structure of actions and predicates.

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

5. **Focus**: 
   - TASK focuses on the logical representation of the problem and the actions required to solve it.
   - CODE_FUNC emphasizes the procedural steps and the flow of execution in a simulation context.

6. **Output**: 
   - TASK does not specify any output; it is concerned with the conditions under which actions can be performed.
   - CODE_FUNC mentions printing relevant messages and checking task completion, indicating a focus on feedback during execution.

7. **Environment Setup**: 
   - TASK includes the initial state of the environment in a structured format.
   - CODE_FUNC describes the setup and resetting of the environment in a more narrative form.

8. **Action Sequence**: 
   - TASK defines actions in a way that can be interpreted by a planner, focusing on the logical sequence of actions.
   - CODE_FUNC describes the sequence of actions in a procedural manner, detailing the order of operations in the simulation.

9. **Interactivity**: 
   - TASK does not imply any interactive elements; it is a static definition of actions and states.
   - CODE_FUNC suggests an interactive simulation where the gripper's actions are executed and monitored.

10. **Implementation**: 
    - TASK is a theoretical construct meant for planning and reasoning about actions.
    - CODE_FUNC is a practical implementation meant to be executed in a programming 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 env import setup_environment, shutdown_environment
from skill_code import pick, pull, rotate, move
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']

        # === Execute the Plan ===
        # Step 1: Rotate the gripper to the correct orientation (90 degrees)
        print("[Task] Rotating gripper to 90 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 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, reward, done = move(env, task, target_pos=positions['bottom_side_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, reward, 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, reward, 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, reward, 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! 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()
-------------------

