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log_bottle_1.txt
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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:
Object state: Object State: fridge(at corner), table(mounted robot_a), bottle(in fridge), cup(on table), robot_a(on table), robot_b(at table)
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
{'1': {'robot_a': 'Wait', 'robot_b': 'Go to the fridge'}, '2': {'robot_a': 'Wait', 'robot_b': 'Open the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Take the bottle out of the fridge'}, '4': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '5': {'robot_a': 'Pick up the cup.', 'robot_b': 'Go to table'}, '6': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Take bottle from robot_a'}, '7': {'robot_a': 'Wait', 'robot_b': 'Put bottle on table'}, '8': {'robot_a': 'Hand over the cup', 'robot_b': 'Take the cup'}}
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: wait_time, robot_a_pos, robot_a_ori.
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: go to the fridge.
robot_a waiting for this task to complete...
********************Success! "go to the fridge" 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: Go to the fridge
Task-related knowledge: bot_b_pos, bot_b_ori, distance_to_fridge.
Response 1:
'''
No code for dry-run
'''
Outcome: Done.
Human feedback: Done
'''
Your response:
save_information("bot_b_pos", current_pos, 1)
save_information("bot_b_ori", current_ori, 1)
save_information("distance_to_fridge", "unknown", 2)
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: wait_time, robot_a_pos, robot_a_ori.
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: open the fridge.
robot_a waiting for this task to complete...
********************Success! "open the fridge" 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: Open the fridge
Task-related knowledge: grasp_pos_fridge, grasp_ori_fridge, pull_distance_fridge.
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: wait_time, robot_a_pos, robot_a_ori.
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: take the bottle out of the fridge.
robot_a waiting for this task to complete...
********************Success! "take the bottle out of the fridge" 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 the bottle out of the fridge
Task-related knowledge: fridge_door_pos, fridge_door_ori, bottle_pos, bottle_ori, remove_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: wait_time, robot_a_pos, robot_a_ori.
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: close the fridge.
robot_a waiting for this task to complete...
current_codeline:move_gripper_to_pose(moveup_pos, current_ori)
Please determine whether the given correction indicates there's an error beyond the task's scope.
Task: open the top shelf
Correction: move right a little bit
Output: no
Task: pick up the apple.
Correction: that's wrong. move left please
Output: no
Task: put the apple into the fridge
Correction: you should open the fridge first
Output: yes
Task: open the fridge
Correction: wrong detection
Output: no
Task: pick up the pen
Correction: no, pick the red pen
Output: yes
Task: robot_b: Close the fridge
Correction: robot cannot close fridge with bottle in hand.
Output:
yes
**Error Type: Planning error
{'1': {'robot_a': 'Wait', 'robot_b': 'Go to the fridge'}, '2': {'robot_a': 'Wait', 'robot_b': 'Open the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Take the bottle out of the fridge'}, '4': {'robot_a': 'Wait', 'robot_b': 'Go to table'}, '5': {'robot_a': 'Pick up the cup.', 'robot_b': 'Hand bottle to robot_a'}, '6': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'}, '7': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '8': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
The fridge has to be closed after use, the wine needs to be poured into the cup and the cup has to be handed over.
Robot cannot close the fridge with the wine bottle in its hand.
{'1': {'robot_a': 'Pick up the cup.', 'robot_b': 'Pass bottle to robot_a'}, '2': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '4': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
File 'cache/history_tmp.pkl' has been deleted successfully.
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"}
}
Instruction: bring a cup of wine.
Response:
{
"1": {'robot_a': 'Wait', 'robot_b': 'Go to the fridge'},
"2": {'robot_a': 'Wait', 'robot_b': 'Open the fridge'},
"3": {'robot_a': 'Wait', 'robot_b': 'Take the bottle out of the fridge'},
"4": {'robot_a': 'Wait', 'robot_b': 'Go to table'},
"5": {'robot_a': 'Pick up the cup.', 'robot_b': 'Hand bottle to robot_a'},
"6": {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'},
"7": {'robot_a': 'Wait', 'robot_b': 'Close the fridge'},
"8": {'robot_a': 'Hand over the cup', '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 fridge has to be closed after use, the wine needs to be poured into the cup and the cup has to be handed over.
5. Robot cannot close the fridge with the wine bottle in its hand.
Object state: fridge(open, at corner), table(mounted robot_a), bottle(in robot_b gripper), cup(on table), robot_a(on table), robot_b(at fridge)
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper'] (No change in object state for 'wait' task)
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
{'1': {'robot_a': 'Pick up the cup.', 'robot_b': 'Pass bottle to robot_a'}, '2': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '4': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
robot_a is performing the task: pick up the cup.
All robots are busy on their tasks.
********************Success! "pick up the cup" 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 the cup.
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, 1)
save_information("pick_ori", current_ori, 1)
save_information("pickup_height", "standard", 1)
Additional waypoint needed: None
defaultdict(<class 'dict'>, {})
--------------------------------------------------------------------------------
File 'cache/tmp_info.pkl' not found.
robot_b is performing the task: pass bottle to robot_a.
All robots are busy on their tasks.
current_codeline:move_gripper_to_pose(moveup_pos, current_ori)
Please determine whether the given correction indicates there's an error beyond the task's scope.
Task: open the top shelf
Correction: move right a little bit
Output: no
Task: pick up the apple.
Correction: that's wrong. move left please
Output: no
Task: put the apple into the fridge
Correction: you should open the fridge first
Output: yes
Task: open the fridge
Correction: wrong detection
Output: no
Task: pick up the pen
Correction: no, pick the red pen
Output: yes
Task: robot_b: Pass bottle to robot_a
Correction: close the fridge first
Output:
yes
**Error Type: Planning error
{'1': {'robot_a': 'Pick up the cup.', 'robot_b': 'Close the fridge'}, '2': {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'}, '3': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'}, '4': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
Fridge must be closed after use.
None
{'1': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '2': {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'}, '3': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'}, '4': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
File 'cache/history_tmp.pkl' has been deleted successfully.
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"}
}
Instruction: bring a cup of wine.
Response:
{
"1": {'robot_a': 'Wait', 'robot_b': 'Go to the fridge'},
"2": {'robot_a': 'Wait', 'robot_b': 'Open the fridge'},
"3": {'robot_a': 'Wait', 'robot_b': 'Take the bottle out of the fridge'},
"4": {'robot_a': 'Wait', 'robot_b': 'Go to table'},
"5": {'robot_a': 'Pick up the cup.', 'robot_b': 'Hand bottle to robot_a'},
"6": {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'},
"7": {'robot_a': 'Wait', 'robot_b': 'Close the fridge'},
"8": {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}
}
Instruction: bring a cup of wine.
Response:
{
"1": {'robot_a': 'Pick up the cup.', 'robot_b': 'Close the fridge'},
"2": {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'},
"3": {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'},
"4": {'robot_a': 'Hand over the cup', '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 fridge has to be closed after use, the wine needs to be poured into the cup and the cup has to be handed over.
5. Robot cannot close the fridge with the wine bottle in its hand.
6. Fridge must be closed after use.
Object state: fridge(open, at corner), table(mounted robot_a), bottle(in robot_b gripper), cup(in robot_a gripper), robot_a(on table), robot_b(at fridge)
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper'] (No change in object state for 'wait' task)
Robot Capacity: robot_a=['mounted base', 'gripper'], robot_b=['mobile base', 'gripper']
{'1': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '2': {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'}, '3': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'}, '4': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
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: wait_time, robot_a_pos, robot_a_ori.
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: close the fridge.
robot_a waiting for this task to complete...
Please determine whether the given correction indicates there's an error beyond the task's scope.
Task: open the top shelf
Correction: move right a little bit
Output: no
Task: pick up the apple.
Correction: that's wrong. move left please
Output: no
Task: put the apple into the fridge
Correction: you should open the fridge first
Output: yes
Task: open the fridge
Correction: wrong detection
Output: no
Task: pick up the pen
Correction: no, pick the red pen
Output: yes
Task: robot_b: Close the fridge
Correction: robot cannot close fridge with bottle in hand. put it down first.
Output:
yes
**Error Type: Planning error
{'1': {'robot_a': 'Wait', 'robot_b': 'Put bottle on table'}, '2': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Pick up bottle'}, '4': {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'}, '5': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'}, '6': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
None
robot_b can't manipulate (close fridge or give to another robot) with bottle in its gripper.
{'1': {'robot_a': 'Wait', 'robot_b': 'Put bottle on table'}, '2': {'robot_a': 'Wait', 'robot_b': 'Close the fridge'}, '3': {'robot_a': 'Wait', 'robot_b': 'Pick up bottle'}, '4': {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'}, '5': {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'}, '6': {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}}
File 'cache/history_tmp.pkl' has been deleted successfully.
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"}
}
Instruction: bring a cup of wine.
Response:
{
"1": {'robot_a': 'Wait', 'robot_b': 'Go to the fridge'},
"2": {'robot_a': 'Wait', 'robot_b': 'Open the fridge'},
"3": {'robot_a': 'Wait', 'robot_b': 'Take the bottle out of the fridge'},
"4": {'robot_a': 'Wait', 'robot_b': 'Go to table'},
"5": {'robot_a': 'Pick up the cup.', 'robot_b': 'Hand bottle to robot_a'},
"6": {'robot_a': 'Pour wine into the cup', 'robot_b': 'Go back to the fridge'},
"7": {'robot_a': 'Wait', 'robot_b': 'Close the fridge'},
"8": {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}
}
Instruction: bring a cup of wine.
Response:
{
"1": {'robot_a': 'Pick up the cup.', 'robot_b': 'Close the fridge'},
"2": {'robot_a': 'Wait', 'robot_b': 'Pass bottle to robot_a'},
"3": {'robot_a': 'Pour wine into the cup', 'robot_b': 'Wait'},
"4": {'robot_a': 'Hand over the cup', 'robot_b': 'Wait'}
}