log_arm_2_2.txt

Imagine you are working with two robot equipped with a two-fingered gripper for grasping. The robots' mission is to collaborate and complete tasks based on a given instructions. However, they may struggle with high-level instructions
Your role is to understand those instructions and break them down into smaller, actionable sub-tasks. You will also assign the tasks to specific robot and order the tasks. If there is ambiguity in deciding the object given existing information, just list all possible objects like Example 1.
If one robot needs to wait for the other to finish a particular sub-task, assign them task "wait" at the same line. 
Here are some examples:

Object state: fridge(closed), green apple(on table), red apple(on table)
Robot capacity: robot_a=['mobile base', 'gripper'], robot_b=['mobile base', 'gripper']
Instruction: put the apple in the fridge.
Response:
{
  "1": {"robot_a" : "Open the fridge.", "robot_b" : "Pick up apple_x." },
  "2": {"robot_a" : "Wait", "robot_b" : "Put apple_x into the fridge.",},
  "3": {"robot_a": "Close the fridge.", "robot_b" : "Wait",},
}

Object state: cabinet(closed)
Robot capacity: robot_a=[], robot_b=[]
Instruction: open the top white cabinet
Response:
{
  "1": {"robot_a": "Open the cabinet.", "robot_b": "Wait"}
}

Object state: N/A
Robot capacity: robot_a=['fixed base', 'gripper'], robot_b=['mobile base', 'gripper']
Instruction: put the spoon into the bottom black shelf
Response:
{
  "1": {"robot_a" : "Open the bottom black shelf.", "robot_b" : "Go to the spoon"},
  "2": {"robot_a" : "Wait", "robot_b" : "Pick up the spoon." },
  "3": {"robot_a" : "Wait", "robot_b": "Go to robot_a"},
  "4": {"robot_a" : "Take spoon", "robot_b" : "Give spoon to robot_a"},
  "5": {"robot_a" : "Put spoon into the bottom black shelf", "robot_b": "Wait"}
  "6": {"robot_a" : "Close the bottom black shelf.", "robot_b" : "Wait"}
}

Rules:
1. In each sub-task of your response, the robot should manipulate one object and only move its gripper once.
2. If you think the instruction is low-level enough, just repeat the instruction in your response
3. Omit assumptions and only follow a json format after the word Response:
4. The apple needs to be stable for the knife to cut it which implies the apple needing to be on a table or solid surface.
5. robot_a can not move away from the table; robot_b only has one gripper and can not hold two items (knife and apple) at the same time.
6. robot_b should not pick up the apple.
7. Robot_b can only hold one item at a time.
8. All tasks happening on the table should be done by robot_a as it is mounted on the table.
9. robot_b cannot perform tasks on table as robot_a is mounted there.
10. Cutting board should be put on the table before putting the apple.
11. robot_a cannot place the apple without the board being placed on the table.

Object state: Object State: table(mounted robot_b), orange(on table), knife(in sink), cutting_board(in sink), robot_a(at table), robot_b(mounted on table)
Robot Capacity: robot_a=['mobile base', 'gripper'], robot_b=['mounted base', 'gripper']

{'1': {'robot_a': 'Go to the sink.', 'robot_b': 'Wait'}, '2': {'robot_a': 'Pick up cutting_board.', 'robot_b': 'Wait'}, '3': {'robot_a': 'Go to table.', 'robot_b': 'Wait'}, '4': {'robot_a': 'Place cutting_board on table.', 'robot_b': 'Wait'}, '5': {'robot_a': 'Go to sink.', 'robot_b': 'Place orange on cutting_board.'}, '6': {'robot_a': 'Pick up knife.', 'robot_b': 'Wait'}, '7': {'robot_a': 'Go to table.', 'robot_b': 'Wait'}, '8': {'robot_a': 'Give knife to robot_b.', 'robot_b': 'Take knife from robot_a'}, '9': {'robot_a': 'Wait', 'robot_b': 'Cut the orange.'}}
robot_a is performing the task: go to the sink.
robot_b waiting for this task to complete...
********************Success! "go to the sink" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Go to the sink.
Task-related knowledge: destination_pos, destination_ori

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

save_information("destination_pos", destination_pos, 0)
save_information("destination_ori", destination_ori, 0)
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: pick up cutting_board.
robot_b waiting for this task to complete...
********************Success! "pick up cutting_board" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Pick up cutting_board.
Task-related knowledge: pick_pos, pick_ori, pickup_height.

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: go to table.
robot_b waiting for this task to complete...
********************Success! "go to table" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Go to table.
Task-related knowledge: destination_coord, move_speed, obstacle_avoidance

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

save_information("destination_coord", None, 1)
save_information("move_speed", None, 1)
save_information("obstacle_avoidance", None, 1)
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: place cutting_board on table.
robot_b waiting for this task to complete...
********************Success! "place cutting_board on table" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Place cutting_board on table.
Task-related knowledge: place_pos, place_ori, place_height.

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: go to sink.
All robots are busy on their tasks.
********************Success! "go to sink" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Go to sink.
Task-related knowledge: destination_pos, destination_ori, travel_path

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

save_information("destination_pos", "sink", 2)
save_information("destination_ori", "towards sink", 2)
save_information("travel_path", [], 2)
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: place orange on cutting_board.
All robots are busy on their tasks.
********************Success! "place orange on cutting_board" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Place orange on cutting_board.
Task-related knowledge: place_pos, place_ori, drop_height

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: pick up knife.
robot_b waiting for this task to complete...
********************Success! "pick up knife" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Pick up knife.
Task-related knowledge: pick_pos, pick_ori, pickup_height

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

save_information("pick_pos", current_pos, 2)
save_information("pick_ori", current_ori, 2)
save_information("pickup_height", "suitable height", 2) 
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: go to table.
robot_b waiting for this task to complete...
********************Success! "go to table" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Go to table.
Task-related knowledge: destination_coord, move_speed, obstacle_avoidance

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

save_information("destination_coord", None, 1)
save_information("move_speed", None, 1)
save_information("obstacle_avoidance", None, 1)
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: wait.
robot_b waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Wait
Task-related knowledge: no_info_required

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No task-related knowledge to save.
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: give knife to robot_b.
All robots are busy on their tasks.
********************Success! "give knife to robot_b" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Give knife to robot_b.
Task-related knowledge: robot_a_pos, robot_a_ori, robot_b_pos, robot_b_ori, handover_pos, handover_ori.

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No additional waypoint needed.
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: take knife from robot_a.
All robots are busy on their tasks.
********************Success! "take knife from robot_a" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Take knife from robot_a
Task-related knowledge: robot_b_position, robot_a_position, grasp_pos, grasp_ori, transfer_distance

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_a is performing the task: wait.
robot_a waiting for this task to complete...
********************Success! "wait" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_a: Wait
Task-related knowledge: No specific information is required as this task implies inactivity for a certain period.

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: cut the orange.
robot_a waiting for this task to complete...
********************Success! "cut the orange" is fulfilled !!!!!********************
--------------------------------------------------------------------------------
*at save_info*
A robot is doing a task, and a human is giving it feedback to help it complete the task correctly. 
Your first task is to use the command 'save_information(info_name, info_value, response_round: int)' to store the correct value of "task-related knowledge".
Your second task is to determine whether there are additional waypoints needed. To do this, you need to distinguish between positional adjustments and waypoints based on analyzing human's feedback.

Below are some examples:
Interaction history:
'''
Task: Insert the pen to the holder, with the pen already in hand.
# Task-related knowledge: insertion_pos, insertion_ori, insertion_depth

Response 1:
'''
insert_pos, insert_ori = get_task_pose("insert the pen to the holder")
move_gripper_to_pose(insert_pos, insert_ori)
current_pos, current_ori = get_current_state()
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Interrupted at codeline "move_gripper_to_pose(insert_pos, insert_ori)".
Human feedback: move forward a bit

Response to feedback 2:
'''
current_pos, current_ori = get_current_state()
target_pos = parse_pos(f"a point a bit backward to {current_pos}")
move_gripper_to_pose(target_pos, current_ori)

'''
Outcome: Done.
Human feedback: move left and tilt up a little bit

Response to feedback 3:
'''
movedown_pos = parse_pos(f"a point 5cm to {current_pos}.")
move_gripper_to_pose(movedown_pos, current_ori)
open_gripper()
'''
Outcome: Done.
Human feedback: Done.
'''

Your response:
save_information("insertion_pos", current_pos, 4)
save_information("insertion_ori", target_ori, 4)
save_information("insertion_depth", "5cm", 5)
Additional waypoint needed: None

Interaction history:
'''
Task: robot_b: Cut the orange.
Task-related knowledge: cut_pos, cut_ori, cutting_depth, sawing_motion, object_grip_strength.

Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''

Your response:

No feedback to respond. Continue with the next action.
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
File 'cache/history_tmp.pkl' has been deleted successfully.
---------------------------Ready to move to next instruction...---------------------------
File 'cache/history_tmp.pkl' not found.