=== Instruction 1 ===
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 relevant objects, specifically a rubbish item, a bin, and a bottom drawer. 

The first step of the task involves pulling the bottom drawer open, and if this action is completed successfully, it proceeds to the next step. The second step involves placing the rubbish into the bin. After attempting to place the rubbish, the function checks if the task is completed successfully and prints the corresponding message. Finally, the environment is shut down to ensure proper cleanup. The function is executed when the script is run as the main program.
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 describes a procedural function for simulating a task in a programming context.
2. **Structure**: TASK is structured in terms of domain definitions, types, predicates, and actions, whereas CODE_FUNC is structured as a sequence of procedural steps in a function.
3. **Purpose**: TASK is designed for automated planning and reasoning about actions in a defined domain, while CODE_FUNC is intended for executing a specific simulation task in a programming environment.
4. **Execution**: TASK does not execute actions but defines them for a planner to use, while CODE_FUNC executes the actions in a specific order as part of a simulation.
5. **Level of Abstraction**: TASK operates at a higher level of abstraction, focusing on the logical relationships and requirements of actions, while CODE_FUNC operates at a lower level, detailing the specific steps to perform the task.
6. **Output**: TASK does not produce output directly; it is used to generate plans, whereas CODE_FUNC includes print statements to provide feedback on task completion.
7. **Environment Setup**: TASK defines the initial state and conditions for planning, while CODE_FUNC includes specific setup and teardown procedures for the simulation environment.
8. **Error Handling**: TASK does not include error handling mechanisms, while CODE_FUNC checks for successful completion of actions and provides feedback accordingly.

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 *  # Importing predefined skills
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['item3']  # Assuming item3 is the rubbish
        bin_pos = positions['bin']  # Assuming bin is the target for rubbish
        bottom_anchor_pos = positions['bottom_anchor_pos']  # Position for the bottom drawer

        # Step 1: Pull the bottom drawer open
        print("[Task] Pulling the bottom drawer open.")
        obs, reward, done = pull(
            env,
            task,
            pull_distance=0.1,  # Adjust distance as needed
            pull_axis='y',  # Assuming pulling along the y-axis
            max_steps=100,
            threshold=0.01,
            timeout=10.0
        )
        if done:
            print("[Task] Task ended after pulling the drawer!")
            return

        # Step 2: Place the rubbish in the bin
        print("[Task] Placing rubbish in the bin.")
        obs, reward, done = place(
            env,
            task,
            target_pos=bin_pos,
            approach_distance=0.15,
            max_steps=100,
            threshold=0.01,
            approach_axis='z',
            timeout=10.0
        )
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

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

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

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 in a robotic environment. It begins by setting up the environment and resetting the task to its initial state. It optionally initializes video recording for the simulation. The function retrieves the positions of various objects in the environment, specifically a bin and a piece of rubbish. 

The task consists of two main steps: first, it attempts to pull open a drawer, and if successful, it proceeds to place the rubbish into the bin. After each action, it checks if the task is completed and prints the corresponding status. Finally, the environment is shut down to ensure proper cleanup after the task execution. The function is executed when the script is run as the main program.
Similarity: {'ok': False, 'reason': 'LLM JSON parsing failed.'}
Differences: 1. **Purpose**: 
   - **TASK**: Describes a specific instruction for a robot to follow, detailing the actions to be taken (pulling a drawer and dropping rubbish).
   - **CODE_FUNC**: Describes a function that implements a simulation of the task, including setup, execution, and cleanup.

2. **Level of Abstraction**:
   - **TASK**: High-level instruction focused on what needs to be done.
   - **CODE_FUNC**: Low-level implementation detailing how the task is executed programmatically.

3. **Components**:
   - **TASK**: Contains a single instruction with no programming constructs.
   - **CODE_FUNC**: Contains multiple programming constructs, including function definitions, environment setup, action execution, and status checks.

4. **Execution Flow**:
   - **TASK**: Represents a sequence of actions without specifying how they are executed.
   - **CODE_FUNC**: Specifies a flow of execution, including initialization, action attempts, checks for completion, and cleanup.

5. **Environment Interaction**:
   - **TASK**: Assumes an environment where the actions can be performed.
   - **CODE_FUNC**: Explicitly interacts with the environment, retrieving object positions and managing state.

6. **Error Handling**:
   - **TASK**: Does not account for errors or failures in execution.
   - **CODE_FUNC**: Implies checks for task completion and may include error handling through status checks.

7. **Output**:
   - **TASK**: Does not produce output; it is a directive.
   - **CODE_FUNC**: Produces output related to task status and completion.

8. **Context**:
   - **TASK**: Contextualized as a single instruction within a robotic task.
   - **CODE_FUNC**: Contextualized as part of a larger program that simulates robotic behavior.

9. **State Management**:
   - **TASK**: Does not manage state; it simply describes actions.
   - **CODE_FUNC**: Manages state through initialization, action execution, and cleanup.

10. **Reusability**:
    - **TASK**: A standalone instruction that may not be reused in different contexts.
    - **CODE_FUNC**: A reusable function that can be called multiple times within a program.

11. **Complexity**:
    - **TASK**: Simple and straightforward.
    - **CODE_FUNC**: More complex due to the inclusion of multiple programming elements and logic.

12. **Language**:
    - **TASK**: Written in natural language.
    - **CODE_FUNC**: Written in a programming language (implied to be Python or similar).

13. **Focus**:
    - **TASK**: Focuses on the "what" of the action.
    - **CODE_FUNC**: Focuses on the "how" of executing the action.

14. **Simulation Aspect**:
    - **TASK**: Does not involve simulation; it is a direct instruction.
    - **CODE_FUNC**: Involves simulation of the task within a defined environment.

15. **Initialization**:
    - **TASK**: No initialization is mentioned.
    - **CODE_FUNC**: Includes initialization of the environment and task state.

16. **Finalization**:
    - **TASK**: No finalization or cleanup is mentioned.
    - **CODE_FUNC**: Includes a shutdown process to clean up after task execution.

17. **Action Sequence**:
    - **TASK**: Specifies a sequence of actions (pull drawer, drop rubbish).
    - **CODE_FUNC**: Implements the sequence programmatically, including checks and status updates.

18. **Dependencies**:
    - **TASK**: Independent of any programming constructs or libraries.
    - **CODE_FUNC**: Dependent on programming constructs, libraries, and the environment setup.

19. **User Interaction**:
    - **TASK**: No user interaction is implied.
    - **CODE_FUNC**: May involve user interaction through status outputs or logging.

20. **Flexibility**:
    - **TASK**: Less flexible; it is a fixed instruction.
    - **CODE_FUNC**: More flexible; can be modified or extended with additional logic or parameters.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as TASK is a high-level directive while CODE_FUNC is a detailed implementation of that directive in a programming context.

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 *  
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:
        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()

        bin_pos = positions['bin']
        rubbish_pos = positions['item3']  

        drawer_pos = positions['bottom_side_pos']  
        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 after pulling the drawer!")
            return

        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

    finally:
        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 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 drawer. 

The task consists of three main steps: first, it attempts to pull open a drawer; second, it picks up the rubbish from a specified position; and third, it places the rubbish into the bin. After each action, it checks if the action was successful and prints the corresponding message. Finally, the environment is shut down to ensure proper cleanup after the task is completed. 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 sequence of actions to be performed (selecting a drawer, picking up rubbish, and disposing of it).
   - **CODE_FUNC**: Describes a function that implements a simulation of the task, including setup, execution, and cleanup.

2. **Structure**: 
   - **TASK**: Presented in a natural language instruction format followed by a PDDL domain definition.
   - **CODE_FUNC**: Presented as a description of a programming function, detailing its operational flow and logic.

3. **Detail Level**: 
   - **TASK**: Provides a high-level overview of the actions without implementation details.
   - **CODE_FUNC**: Includes specific implementation details, such as environment setup, action success checks, and cleanup procedures.

4. **Execution Context**: 
   - **TASK**: Implies a theoretical or planned execution of actions.
   - **CODE_FUNC**: Implies actual execution within a programming environment, including potential interaction with a simulation framework.

5. **Action Sequence**: 
   - **TASK**: Lists actions in a straightforward manner (open drawer, pick up rubbish, dispose of it).
   - **CODE_FUNC**: Describes the actions in the context of a function, including checks for success and the order of operations.

6. **Outcome Handling**: 
   - **TASK**: Does not specify how to handle the outcomes of actions.
   - **CODE_FUNC**: Mentions checking for success after each action and printing messages based on the results.

7. **Environment Management**: 
   - **TASK**: Does not address the management of the simulation environment.
   - **CODE_FUNC**: Explicitly mentions resetting the environment and shutting it down after task completion.

8. **Initialization**: 
   - **TASK**: Does not include details about initializing the simulation or task state.
   - **CODE_FUNC**: Includes steps for initializing the environment and optionally recording the simulation.

9. **Object Retrieval**: 
   - **TASK**: Does not specify how objects are identified or retrieved.
   - **CODE_FUNC**: Mentions retrieving positions of specific objects as part of the setup.

10. **Execution Trigger**: 
    - **TASK**: Does not specify how or when the task is executed.
    - **CODE_FUNC**: States that the function is executed when the script is run as the main program.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as they serve different purposes and contexts within the domain of object manipulation and simulation.

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 *
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['item3']  # Assuming item3 is the rubbish
        bin_pos = positions['bin']  # Assuming bin is the trash can

        # Step 1: Pull the drawer open
        print("[Task] Pulling the drawer open.")
        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] Drawer opened successfully!")
        else:
            print("[Task] Failed to open the drawer.")

        # Step 2: Pick up the rubbish
        print("[Task] Picking up the rubbish.")
        obs, _, done = pick(env, task, target_pos=rubbish_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Rubbish picked successfully!")
        else:
            print("[Task] Failed to pick up the rubbish.")

        # Step 3: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin.")
        obs, _, done = place(env, task, target_pos=bin_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Rubbish placed in the bin successfully!")
        else:
            print("[Task] Failed to place the rubbish in the bin.")

    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 agent. It begins by setting up the environment and resetting the task to its initial state. The simulation can optionally record video of the task's execution. 

The function retrieves the positions of relevant objects, including rubbish and a bin. It then proceeds through a series of steps: first, the agent pulls a drawer to unlock a cabinet, followed by picking up rubbish, and finally placing the rubbish in the bin. After each step, it checks if the task is completed and prints appropriate messages. 

Regardless of the outcome, the environment is 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 sequence of actions to be performed by a robotic agent in a PDDL format, focusing on unlocking a cabinet and disposing of rubbish.
   - CODE_FUNC describes a function that implements a simulation of the task, including setup, execution, and cleanup.

2. **Format**: 
   - TASK is written in PDDL (Planning Domain Definition Language), which is a formal language used for expressing planning problems.
   - CODE_FUNC is written in a programming language (likely Python), which is used to implement the logic of the task in a simulation environment.

3. **Detail Level**: 
   - TASK provides a detailed breakdown of actions, preconditions, and effects in a structured manner suitable for automated planning.
   - CODE_FUNC provides a high-level overview of the task execution process without detailing the underlying logic of each action.

4. **Execution Context**: 
   - TASK is designed to be interpreted by a planner that can execute the defined actions based on the specified conditions.
   - CODE_FUNC is meant to be executed as a standalone program, simulating the task in a controlled environment.

5. **State Management**: 
   - TASK defines the initial state and the effects of actions in terms of predicates and their relationships.
   - CODE_FUNC manages state through procedural programming, checking conditions and updating the state as the simulation progresses.

6. **Output**: 
   - TASK does not specify any output; it focuses on the planning aspect.
   - CODE_FUNC includes print statements to provide feedback on the task's progress and completion.

7. **Flexibility**: 
   - TASK is rigid in its structure, adhering to the rules of PDDL for planning.
   - CODE_FUNC can be modified more easily to change the simulation behavior or add features like video recording.

8. **Error Handling**: 
   - TASK does not include any error handling mechanisms; it assumes a perfect execution of the defined actions.
   - CODE_FUNC may include checks and messages to handle different outcomes and errors during the simulation.

9. **Action Representation**: 
   - TASK explicitly defines actions with parameters, preconditions, and effects.
   - CODE_FUNC describes actions in a more narrative form, outlining the sequence without formal definitions.

10. **Environment Interaction**: 
    - TASK focuses on the logical representation of actions and their effects on the environment.
    - CODE_FUNC includes practical interaction with the environment, such as pulling drawers and picking up objects, as part of a simulation.

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

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 *
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['item1']  # Assuming item1 is the rubbish
        bin_pos = positions['bin']  # Assuming bin is the disposal location
        bottom_anchor_pos = positions['bottom_anchor_pos']  # Position for pulling the drawer

        # Step 1: Pull the drawer to unlock the cabinet
        print("[Task] Pulling the drawer to unlock the cabinet.")
        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 after pulling the drawer!")
            return

        # Step 2: Pick up the rubbish
        print("[Task] Picking up the rubbish.")
        obs, reward, done = pick(env, task, target_pos=rubbish_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 up the rubbish!")
            return

        # Step 3: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin.")
        obs, reward, done = place(env, task, target_pos=bin_pos, approach_distance=0.15, max_steps=100, threshold=0.01, approach_axis='z', timeout=10.0)
        if done:
            print("[Task] Task completed successfully! Reward:", reward)
        else:
            print("[Task] Task not completed yet (done=False).")

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

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

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 relevant objects, specifically a piece of rubbish and a bin. 

The task consists of three main steps: first, it attempts to pull open a drawer; second, it picks up the rubbish from a table; and third, it places the rubbish into the bin. After each action, it checks if the action was successful and prints the corresponding message. Finally, the environment is shut down to ensure proper cleanup after the task is completed. 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 follow, focusing on the actions of opening a drawer and disposing of rubbish.
   - **CODE_FUNC**: Describes a function that implements a simulation of the task, including setup, execution, and cleanup.

2. **Structure**: 
   - **TASK**: Presented in a natural language format as an instruction.
   - **CODE_FUNC**: Presented in a programming context, detailing a function with specific programming constructs.

3. **Detail Level**: 
   - **TASK**: Provides a high-level overview of the actions to be performed without technical details.
   - **CODE_FUNC**: Includes technical details about the simulation process, such as environment setup, action success checks, and cleanup procedures.

4. **Execution Context**: 
   - **TASK**: Implies a direct action to be taken by a robot or agent.
   - **CODE_FUNC**: Implies a scripted execution within a programming environment, including function calls and potential error handling.

5. **Action Sequence**: 
   - **TASK**: Focuses on the sequence of actions (open drawer, dispose of rubbish).
   - **CODE_FUNC**: Describes a more complex sequence that includes checking for success after each action and managing the simulation environment.

6. **Outcome Handling**: 
   - **TASK**: Does not specify how to handle the outcomes of actions.
   - **CODE_FUNC**: Includes checks for the success of actions and prints messages based on those outcomes.

7. **Environment Management**: 
   - **TASK**: Does not mention any management of the simulation environment.
   - **CODE_FUNC**: Explicitly mentions initializing and shutting down the environment, indicating a focus on simulation integrity.

8. **Initialization**: 
   - **TASK**: Assumes the environment is already set up and ready for action.
   - **CODE_FUNC**: Includes steps to reset the task to its initial state and optionally initialize video recording.

9. **Object Interaction**: 
   - **TASK**: Mentions interacting with a drawer and rubbish but does not detail the interactions.
   - **CODE_FUNC**: Specifies the retrieval of object positions and the actions taken with those objects in a simulation context.

10. **Output**: 
    - **TASK**: Does not produce any output or feedback.
    - **CODE_FUNC**: Produces output messages based on the success of actions taken during the simulation.

In summary, the semantic differences between TASK and CODE_FUNC are significant, as TASK is a high-level instruction while CODE_FUNC is a detailed implementation of that instruction in a programming context.

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 *
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['item1']  # Assuming 'item1' is the rubbish
        bin_pos = positions['bin']  # Assuming 'bin' is the target for rubbish

        # === Execute the Plan ===
        # Step 1: Pull open any available drawer
        print("[Task] Pulling open the drawer...")
        obs, _, done = pull(env, task, pull_distance=0.1, pull_axis='y')  # Adjust parameters as needed
        if done:
            print("[Task] Drawer opened successfully!")
        else:
            print("[Task] Failed to open the drawer.")

        # Step 2: Pick up the rubbish from the table
        print("[Task] Picking up the rubbish...")
        obs, _, done = pick(env, task, target_pos=rubbish_pos, approach_distance=0.15)
        if done:
            print("[Task] Rubbish picked successfully!")
        else:
            print("[Task] Failed to pick up the rubbish.")

        # Step 3: Place the rubbish in the bin
        print("[Task] Placing the rubbish in the bin...")
        obs, _, done = place(env, task, target_pos=bin_pos, approach_distance=0.15)
        if done:
            print("[Task] Rubbish placed in the bin successfully!")
        else:
            print("[Task] Failed to place the rubbish in the bin.")

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

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

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

