=== Instruction 1 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation 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 key objects, including rubbish, a bin, and a switch. 

The task consists of four main steps: closing a gripper, pressing a switch to turn on a light, picking up rubbish, and placing the rubbish in a bin. After each step, it checks if the task is completed and prints appropriate messages. Finally, it ensures that the environment is properly shut down before concluding 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 domain and problem for a robotic disposal task using PDDL, focusing on the planning aspects of the robot's actions.
   - CODE_FUNC describes a function that simulates the execution of the task in a robotic environment, focusing on the operational execution of the task.

2. **Structure**: 
   - TASK is structured in a formal PDDL format, defining actions, predicates, and the initial state of the environment.
   - CODE_FUNC is structured as a programming function, detailing procedural steps to execute the task.

3. **Content**: 
   - TASK includes definitions of actions (pick, place, close_gripper, press) with preconditions and effects.
   - CODE_FUNC outlines a sequence of operations (closing gripper, pressing switch, picking up rubbish, placing rubbish) without detailing preconditions and effects.

4. **Execution vs. Planning**: 
   - TASK is focused on planning and defining the rules for the robot's actions.
   - CODE_FUNC is focused on executing those plans in a simulated environment.

5. **Environment Setup**: 
   - TASK specifies the initial state of the environment in terms of predicates.
   - CODE_FUNC describes the setup and resetting of the environment programmatically.

6. **Feedback Mechanism**: 
   - TASK does not include any feedback mechanism; it is purely declarative.
   - CODE_FUNC includes checks and prints messages to indicate the progress and completion of the task.

7. **Simulation vs. Definition**: 
   - TASK is a definition of the problem and domain for planning.
   - CODE_FUNC is a simulation of the defined task, executing the actions in a specific order.

8. **Language**: 
   - TASK uses PDDL, a domain-specific language for planning.
   - CODE_FUNC uses a general-purpose programming language (not specified, but implied to be Python or similar).

9. **Focus on Conditions**: 
   - TASK explicitly defines conditions under which actions can be performed.
   - CODE_FUNC assumes those conditions are handled within the simulation without explicitly stating them.

10. **Finalization**: 
    - TASK does not include any finalization steps; it is a static definition.
    - CODE_FUNC includes steps to ensure proper shutdown of the environment after task completion.

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 close_gripper, press, 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
        rubbish_pos = positions['rubbish']
        bin_pos = positions['bin']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick up the rubbish
        print("[Task] Picking up the rubbish...")
        obs, reward, done = pick(env, task, rubbish_pos)
        if done:
            print("[Task] Task ended after picking up the rubbish!")
            return

        # Step 4: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin...")
        obs, reward, done = place(env, task, bin_pos)
        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, including rubbish, a bin, and a switch. 

The task consists of four main steps: closing a gripper, pressing a switch to turn on a light, picking up rubbish, and placing the rubbish into a bin. After each step, it checks if the task is completed and prints appropriate messages. Finally, it ensures that the environment is properly shut down after the task execution. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: TASK defines a specific problem in a planning domain for a robot to execute, while CODE_FUNC describes a function that simulates the execution of a task involving object manipulation.
2. **Structure**: TASK is structured in PDDL (Planning Domain Definition Language) format, which is used for defining planning problems, whereas CODE_FUNC is written in a programming language (likely Python) and describes procedural steps.
3. **Actions**: TASK specifies actions (pick, place, close_gripper, press) with preconditions and effects, while CODE_FUNC outlines a sequence of steps without formal preconditions and effects.
4. **Environment Setup**: TASK includes a detailed definition of the environment and initial conditions in PDDL, while CODE_FUNC mentions setting up the environment but does not provide a formal structure for it.
5. **Execution Flow**: TASK does not specify how the actions are executed or in what order, while CODE_FUNC explicitly describes the order of operations (closing gripper, pressing switch, picking up rubbish, placing rubbish).
6. **Feedback Mechanism**: CODE_FUNC includes checks and messages after each step to indicate progress, which is not present in TASK.
7. **Simulation Aspect**: CODE_FUNC mentions video recording and simulation, which is not a feature of TASK.
8. **Completion Check**: CODE_FUNC includes a mechanism to check if the task is completed, while TASK does not have such a feature.
9. **Shutdown Procedure**: CODE_FUNC describes ensuring the environment is shut down after execution, which is not addressed in TASK.

In summary, the two have distinct purposes, structures, and functionalities, leading to multiple semantic differences.

Refined Code Generated:
-------------------
import numpy as np
from env import setup_environment, shutdown_environment
from skill_code import close_gripper, press, 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
        rubbish_pos = positions['rubbish']
        bin_pos = positions['bin']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick up the rubbish
        print("[Task] Picking up the rubbish...")
        obs, reward, done = pick(env, task, rubbish_pos)
        if done:
            print("[Task] Task ended after picking up the rubbish!")
            return

        # Step 4: Place the rubbish into the bin
        print("[Task] Placing the rubbish into the bin...")
        obs, reward, done = place(env, task, bin_pos)
        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 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 key objects, including rubbish, a bin, and a switch. 

The task consists of four main steps: closing a gripper, pressing a switch to turn on a light, picking up rubbish, and placing the rubbish in a bin. After each step, it checks if the task is completed and prints appropriate messages. Finally, it ensures that the environment is properly shut down before concluding 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 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 robotic task, detailing the steps involved in the process.

2. **Structure**: 
   - TASK is presented in a formal PDDL (Planning Domain Definition Language) format, defining actions, predicates, and the problem setup.
   - CODE_FUNC is a narrative description of a function that runs a simulation, focusing on procedural steps rather than formal definitions.

3. **Detail Level**: 
   - TASK includes specific preconditions and effects for actions (e.g., conditions under which the robot can pick up or place objects).
   - CODE_FUNC provides a high-level overview of the task steps without detailing the underlying conditions or effects.

4. **Execution Context**: 
   - TASK is a declarative representation meant for planning and reasoning about actions in a robotic domain.
   - CODE_FUNC is an imperative description that implies execution in a programming environment, focusing on simulation rather than planning.

5. **Components**: 
   - TASK includes definitions of types, predicates, and actions relevant to the robotic domain.
   - CODE_FUNC mentions specific actions (closing gripper, pressing switch, etc.) but does not define them formally or include the necessary predicates.

6. **Outcome**: 
   - TASK is concerned with the logical representation of actions and their relationships in a planning context.
   - CODE_FUNC emphasizes the procedural execution of tasks and the management of the simulation environment.

7. **Focus**: 
   - TASK focuses on the logical structure and requirements for robotic actions.
   - CODE_FUNC focuses on the operational flow and the sequence of actions to achieve a task in a simulated environment.

8. **Language**: 
   - TASK uses PDDL syntax and terminology specific to planning and robotics.
   - CODE_FUNC uses natural language to describe the function and its purpose, making it more accessible to a general audience.

9. **Simulation vs. Planning**: 
   - TASK is primarily about planning actions based on conditions and effects.
   - CODE_FUNC is about simulating those actions in a controlled environment, including feedback and state management.

10. **Initialization**: 
    - TASK initializes the state of the world through PDDL's `:init` section.
    - CODE_FUNC mentions resetting the task to its initial state but does not specify how this is done in a formal manner.

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 pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment
from skill_code import close_gripper, press, 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
        rubbish_pos = positions['rubbish']
        bin_pos = positions['bin']
        switch_pos = positions['switch']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick up the rubbish
        print("[Task] Picking up the rubbish...")
        obs, reward, done = pick(env, task, rubbish_pos)
        if done:
            print("[Task] Task ended after picking up the rubbish!")
            return

        # Step 4: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin...")
        obs, reward, done = place(env, task, bin_pos)
        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 4 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation 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 specific objects, including rubbish, a switch, and a bin. 

The task consists of four main steps: closing a gripper, pressing a switch to turn on a light, picking up rubbish, and placing the rubbish in a bin. After each step, it checks if the task is completed and prints appropriate messages. Finally, it ensures that the environment is properly shut down before concluding 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 instruction for a robot to follow, while CODE_FUNC describes a function that implements a simulation of a robotic task.
2. **Structure**: TASK is structured in PDDL format, defining actions, predicates, and the problem domain, whereas CODE_FUNC is a narrative description of a function's behavior in a programming context.
3. **Detail Level**: TASK provides detailed preconditions and effects for actions in a formalized way, while CODE_FUNC summarizes the steps without specifying preconditions or effects.
4. **Execution Context**: TASK is a declarative specification meant for planning and execution in a robotic domain, while CODE_FUNC is an imperative description of a function that runs a simulation.
5. **Focus**: TASK focuses on the logical representation of actions and states in a robotic environment, while CODE_FUNC focuses on the procedural steps to execute a task in a programming environment.
6. **Output**: TASK does not specify outputs or results of actions, while CODE_FUNC mentions checking for task completion and printing messages.
7. **Environment Setup**: TASK assumes an initial state and conditions for the robot, while CODE_FUNC describes the setup and reset of the environment as part of the function execution.
8. **Action Sequence**: TASK outlines a specific action (throwing away trash) without detailing the sequence, while CODE_FUNC explicitly lists the sequence of actions taken to complete the 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 close_gripper, press, 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()

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch to turn on the light
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, positions['switch'])
        if done:
            print("[Task] Task ended after pressing the switch!")
            return

        # Step 3: Pick up the rubbish
        print("[Task] Picking up the rubbish...")
        obs, reward, done = pick(env, task, 'rubbish', 'table')
        if done:
            print("[Task] Task ended after picking up the rubbish!")
            return

        # Step 4: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin...")
        obs, reward, done = place(env, task, 'rubbish', 'bin')
        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 5 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function to run a simulation task involving a robotic agent. It begins by setting up the environment and resetting the task to its initial state. The task includes several steps: closing a gripper, pressing a switch, picking up rubbish, and placing the rubbish in a bin. After each action, it checks if the task is completed and prints appropriate messages. The code also initializes video recording for the simulation and retrieves object positions for the actions. Finally, it ensures that the environment is properly shut down after the task is completed or if an error occurs.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK describes a specific instruction for a robotic agent to perform (chucking rubbish).
   - CODE_FUNC outlines a function that simulates a series of actions to complete a task.

2. **Content**: 
   - TASK includes PDDL domain and problem definitions, specifying actions, preconditions, and effects.
   - CODE_FUNC describes a procedural implementation of a simulation task, detailing the sequence of actions and checks.

3. **Structure**: 
   - TASK is structured in a formal language (PDDL) for planning and action representation.
   - CODE_FUNC is written in a programming language (likely Python or similar) for executing a simulation.

4. **Execution**: 
   - TASK does not execute actions; it defines them for a planner to use.
   - CODE_FUNC executes a series of actions in a simulation environment.

5. **Focus**: 
   - TASK focuses on the logical representation of actions and their relationships.
   - CODE_FUNC focuses on the operational aspect of running a simulation and managing state.

6. **Error Handling**: 
   - TASK does not include error handling mechanisms.
   - CODE_FUNC includes provisions for handling errors and ensuring proper shutdown of the environment.

7. **Initialization**: 
   - TASK initializes the state of the world through PDDL definitions.
   - CODE_FUNC initializes the simulation environment and resets the task state programmatically.

8. **Output**: 
   - TASK does not produce output; it is a definition.
   - CODE_FUNC produces output messages based on the completion of actions and task status.

9. **Environment Interaction**: 
   - TASK defines how the robot interacts with objects and locations abstractly.
   - CODE_FUNC specifies the actual interaction with the environment during the simulation.

10. **Action Sequence**: 
    - TASK defines actions in a general sense without a specific sequence.
    - CODE_FUNC specifies a particular sequence of actions to be performed in the simulation.

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 close_gripper, press, 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
        rubbish_pos = positions['rubbish']
        switch_pos = positions['switch']
        bin_pos = positions['bin']

        # Step 1: Close the gripper
        print("[Task] Closing gripper...")
        obs, reward, done = close_gripper(env, task)
        if done:
            print("[Task] Task ended after closing gripper!")
            return

        # Step 2: Press the switch
        print("[Task] Pressing the switch...")
        obs, reward, done = press(env, task, switch_pos)
        if done:
            print("[Task] Task ended after pressing switch!")
            return

        # Step 3: Pick up the rubbish
        print("[Task] Picking up the rubbish...")
        obs, reward, done = pick(env, task, rubbish_pos)
        if done:
            print("[Task] Task ended after picking up rubbish!")
            return

        # Step 4: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin...")
        obs, reward, done = place(env, task, bin_pos)
        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 =====")
-------------------

