=== 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 after the task execution. The function is executed when the script is run directly.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - TASK defines a 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 a robotic task, detailing the procedural steps taken during the simulation.

2. **Structure**: 
   - TASK is structured in a formal PDDL format, defining actions, predicates, and the initial state of the environment.
   - CODE_FUNC is written in a programming language (likely Python), outlining a function with procedural code to execute the task.

3. **Level of Abstraction**: 
   - TASK operates at a higher level of abstraction, focusing on the planning and logical representation of actions and states.
   - CODE_FUNC operates at a lower level, detailing the specific implementation and execution of the task in a simulation environment.

4. **Execution vs. Planning**: 
   - TASK is concerned with the planning phase, where the robot's actions are defined and the conditions for those actions are specified.
   - CODE_FUNC is focused on the execution phase, where the defined actions are carried out in a simulated environment.

5. **Environment Setup**: 
   - TASK includes the definition of the environment, objects, and their relationships in a declarative manner.
   - CODE_FUNC involves procedural setup and initialization of the environment, including optional video recording.

6. **Feedback Mechanism**: 
   - TASK does not provide feedback or output messages; it simply defines the actions and states.
   - CODE_FUNC includes print statements to provide feedback on the completion of each step in the task.

7. **Action Sequence**: 
   - TASK defines actions in a general sense without specifying the order of execution.
   - CODE_FUNC specifies a particular sequence of actions (closing gripper, pressing switch, picking up rubbish, placing rubbish) that must be followed during the simulation.

8. **Error Handling**: 
   - TASK does not address error handling or conditions that may prevent actions from being executed.
   - CODE_FUNC may include checks to determine if the task is completed and handle the environment shutdown, implying some level of error handling.

9. **State Representation**: 
   - TASK uses predicates to represent the state of the environment and the robot.
   - CODE_FUNC may use variables and data structures to represent the state during the simulation.

10. **Domain Specificity**: 
    - TASK is specific to the domain of robotic disposal and is designed for planning in that context.
    - CODE_FUNC is more general in its approach, focusing on the simulation of tasks which could potentially apply to various domains beyond just disposal.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and operate at different levels of abstraction within the context of robotic task execution and planning.

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
        _, 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 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 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 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 problem in a planning domain for a robot to execute, while CODE_FUNC describes a function that simulates the execution of a task in a robotic environment.
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 its objects, while CODE_FUNC focuses on the simulation of the task execution without detailing the environment's formal structure.
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 for task completion and prints messages, whereas TASK does not include any feedback mechanism or output statements.
7. **Initialization**: CODE_FUNC mentions resetting the task to its initial state and optionally initializing video recording, which is not present in TASK.
8. **Main Program Execution**: CODE_FUNC specifies that it runs as the main program, while TASK does not have a concept of execution context.

These differences highlight the distinction between a formal planning problem definition and a procedural simulation function.

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
        _, 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 =====")
-------------------

=== Instruction 3 ===
Is Correct: False
Syntax Pass: False
Code Function: The code defines a function that simulates a task in a virtual 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, including rubbish, a bin, and a switch.

The task consists of four main steps: first, it closes a gripper; second, it presses a switch to turn on a light; third, it picks up rubbish; and finally, it places the rubbish in a bin. After each step, it checks if the task is completed and prints appropriate messages. If the task ends at any step, it exits early. Regardless of the outcome, the environment is shut down at the end of the function. 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 task in a virtual environment.

2. **Structure**: 
   - TASK is defined using PDDL (Planning Domain Definition Language) which includes domain and problem definitions.
   - CODE_FUNC is described in a programming context, detailing a function with procedural steps.

3. **Components**: 
   - TASK includes predicates, actions, and their preconditions/effects, focusing on the logical representation of actions.
   - CODE_FUNC includes procedural steps, object retrieval, and state management, focusing on the execution of actions.

4. **Execution Flow**: 
   - TASK does not specify execution flow; it defines conditions under which actions can occur.
   - CODE_FUNC explicitly outlines a sequence of actions (closing gripper, pressing switch, picking up rubbish, placing rubbish) and checks for completion.

5. **Environment Interaction**: 
   - TASK defines the environment in terms of states and actions without detailing how to interact with it.
   - CODE_FUNC describes interaction with the environment, including initializing states and shutting down the environment.

6. **Error Handling**: 
   - TASK does not include any error handling or early exit conditions.
   - CODE_FUNC includes checks for task completion and early exit if the task ends at any step.

7. **Output**: 
   - TASK does not produce output; it defines a problem for a planner to solve.
   - CODE_FUNC includes print statements to provide feedback on the task's progress and completion.

8. **Initialization**: 
   - TASK initializes the state of the world through the PDDL problem definition.
   - CODE_FUNC initializes the environment and optionally sets up video recording.

9. **Focus**: 
   - TASK focuses on the logical representation of actions and their relationships.
   - CODE_FUNC focuses on the procedural execution of those actions in a simulated environment.

10. **Language**: 
    - TASK is written in PDDL, a domain-specific language for planning.
    - CODE_FUNC is written in a general-purpose programming language, likely Python or similar.

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
        _, 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 that runs 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 key steps: closing a gripper, pressing a switch to turn on a light, 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 function 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 defines a specific instruction for a robot to follow, while CODE_FUNC describes a function that implements a simulation of that task.
2. **Structure**: TASK is structured in PDDL format, defining actions, predicates, and the problem domain, whereas CODE_FUNC is written in a programming language (likely Python) and outlines a procedural function.
3. **Detail Level**: TASK provides a high-level description of the robot's actions and constraints, while CODE_FUNC includes detailed steps and checks for executing the task in a simulation environment.
4. **Execution Context**: TASK is a theoretical model for planning and reasoning about actions, while CODE_FUNC is an executable code that runs a simulation of the task.
5. **Action Sequence**: TASK specifies the actions in a logical order based on conditions, while CODE_FUNC explicitly lists the actions in a procedural manner, including checks and messages after each action.
6. **Error Handling**: CODE_FUNC includes mechanisms for error handling and ensuring proper shutdown of the environment, which is not addressed in TASK.
7. **Initialization**: CODE_FUNC includes initialization steps for the simulation environment, which are not present in TASK.
8. **Output**: CODE_FUNC includes print statements for feedback during execution, while TASK does not specify any output or feedback mechanism.

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']
        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 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 5 ===
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 switch, and a bin. 

The task consists of four main steps: closing a gripper, pressing a switch, 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 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.
   - CODE_FUNC outlines a function that simulates the execution of a series of actions related to object manipulation.

2. **Structure**: 
   - TASK is presented in a PDDL format defining a domain and problem.
   - CODE_FUNC is described in a narrative format explaining the function's purpose and steps.

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

4. **Execution Context**: 
   - TASK is a declarative specification meant for planning and execution by a robot.
   - CODE_FUNC is an imperative description of a function that runs a simulation.

5. **Components**: 
   - TASK includes definitions of actions, predicates, and types relevant to the robot's environment.
   - CODE_FUNC focuses on the procedural steps taken during the simulation, such as closing the gripper and pressing a switch.

6. **Outcome**: 
   - TASK aims to define a problem for a planner to solve.
   - CODE_FUNC aims to execute a series of actions and provide feedback on the task's completion.

7. **Environment Setup**: 
   - TASK specifies the initial state of the environment in a structured way.
   - CODE_FUNC mentions resetting the environment but does not detail the initial state in a formal manner.

8. **Feedback Mechanism**: 
   - TASK does not include any feedback mechanism; it is purely about defining actions and states.
   - CODE_FUNC includes print statements to provide feedback on the task's progress and completion.

9. **Action Sequence**: 
   - TASK defines actions in a way that can be interpreted by a planner.
   - CODE_FUNC explicitly lists the sequence of actions to be performed in the simulation.

10. **Focus on Conditions**: 
    - TASK emphasizes the conditions under which actions can be performed (preconditions).
    - CODE_FUNC does not explicitly mention conditions for actions, focusing instead on the execution flow.

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 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 =====")
-------------------

