building_name stringclasses 1
value | searched_sg dict | task stringlengths 17 196 | task_category stringclasses 4
values | problem stringlengths 2.16k 11.1k | answer stringlengths 28 969 |
|---|---|---|---|---|---|
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"dimitys_office↔desk3",
"dimitys_office↔cabinet3",
"mobile_robotics_lab↔agent",
"desk3↔K31X",
"desk3↔buzzer",
"cabinet3↔apple2"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mob... | Find me object K31X. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(dimitys_office), access(desk3), pickup(K31X), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Find me a carrot. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(fridge), access(fridge), pickup(carrot), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"postdoc_bay1↔desk31",
"postdoc_bay1↔desk32",
"postdoc_bay2↔desk33",
"postdoc_bay2↔desk34",
"postdoc_bay3↔desk35",
"postdoc_bay3↔desk36",
"mobile_robotics_lab↔agent",
"desk35↔dorittos1",
"desk35↔marker",
... | Find me anything purple in the postdoc bays. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(postdoc_bay1), access(desk31), access(desk32), goto(postdoc_bay2), access(desk33), access(desk34), goto(postdoc_bay3), access(desk35), access(desk36), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"cafeteria↔lunch_table",
"mob... | Find me a ripe banana. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(cafeteria), access(lunch_table), pickup(banana1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"robot_lounge2↔toolbox",
"mobile_robotics_lab↔agent",
"toolbox↔screwdiver1"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [
{
... | Find me something that has a screwdriver in it. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(robot_lounge2), open(toolbox), access(toolbox), pickup(screwdiver1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"michaels_office↔desk6",
"michaels_office↔cabinet6",
"mobile_robotics_lab↔agent",
"desk6↔scissors",
"cabinet6↔terminator_poster",
"cabinet6↔stapler3"
],
"nodes": {
"agent": [
{
"id": "agent",
... | One of the offices has a poster of the Terminator. Which one is it? | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(michaels_office), open(cabinet6), access(cabinet6), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"printing_zone2↔printer2",
"mobile_robotics_lab↔agent",
"printer2↔document"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [
{
... | I printed a document but I don't know which printer has it. Find the document. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(printing_zone2), access(printer2), pickup(document), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"meeting_room3↔table1",
"meeting_room3↔table6",
"mobile_robotics_lab↔agent",
"table1↔headphones"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"a... | I left my headphones in one of the meeting rooms. Locate them. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(meeting_room3), access(table1), pickup(headphones), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"phd_bay3↔desk19",
"phd_bay3↔desk20",
"phd_bay3↔desk21",
"phd_bay3↔desk22",
"phd_bay3↔desk23",
"phd_bay3↔desk24",
"mobile_robotics_lab↔agent",
"desk21↔drone1"
],
"nodes": {
"agent": [
{
... | Find the PhD bay that has a drone in it. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(phd_bay3), access(desk21), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"agriculture_lab↔produce_container",
"mobile_robotics_lab↔agent",
"produce_container↔kale_leaves1"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"ass... | Find the kale that is not in the kitchen. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(agriculture_lab), open(produce_container), access(produce_container), pickup(kale_leaves1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"tobis_office↔desk38",
"filipes_office↔desk37",
"mobile_robotics_lab↔agent",
"desk38↔pepsi",
"desk37↔stapler2"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
... | Find me an office that does not have a cabinet. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(tobis_office), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"nikos_office↔desk1",
"nikos_office↔chair1",
"nikos_office↔cabinet1",
"mobile_robotics_lab↔agent",
"desk1↔coffee_mug"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_l... | Find me an office that contains a cabinet, a desk, and a chair. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(nikos_office), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"manipulation_lab↔table3",
"mobile_robotics_lab↔agent",
"table3↔book1",
"table3↔gripper"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [... | Find a book that was left next to a robotic gripper. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(manipulation_lab), access(table3), pickup(book1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"filipes_office↔desk37",
"mobile_robotics_lab↔agent",
"desk37↔stapler2"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [
{
"aff... | Luis gave one of his neighbours a stapler. Find the stapler. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(peters_office), open(cabinet2), access(cabinet2), pickup(stapler), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"meeting_room2↔chair2",
"mobile_robotics_lab↔agent"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [
{
"affordances": [
"... | There is a meeting room with a chair but no table. Locate it. | task_Office_simple_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(meeting_room2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Find object J64M. J64M should be kept at below 0 degrees Celsius. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(fridge), access(fridge), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Find me something non vegetarian. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(fridge), access(fridge), pickup(chicken_kebab), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"robot_lounge2↔toolbox",
"michaels_office↔desk6",
"michaels_office↔cabinet6",
"cafeteria↔lunch_table",
"mobile_robotics_lab↔agent",
"desk6↔scissors",
"cabinet6↔terminator_poster",
"cabinet6↔stapler3",
"lu... | Locate something sharp. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(cafeteria), access(lunch_table), pickup(knife), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"meeting_room4↔table2",
"mobile_robotics_lab↔agent",
"table2↔janga",
"table2↔risk",
"table2↔monopoly"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
... | Find the room where people are playing board games. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(meeting_room4), access(table2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"michaels_office↔desk6",
"michaels_office↔cabinet6",
"mobile_robotics_lab↔agent",
"desk6↔scissors",
"cabinet6↔terminator_poster",
"cabinet6↔stapler3"
],
"nodes": {
"agent": [
{
"id": "agent",
... | Find an office of someone who is clearly a fan of Arnold Schwarzenegger. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(michaels_office), access(cabinet6), open(cabinet6), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"postdoc_bay1↔desk31",
"postdoc_bay1↔desk32",
"postdoc_bay2↔desk33",
"postdoc_bay2↔desk34",
"postdoc_bay3↔desk35",
"postdoc_bay3↔desk36",
"mobile_robotics_lab↔agent",
"desk33↔frame3",
"desk35↔dorittos1",
... | There is a postdoc that has a pet Husky. Find the desk that's most likely theirs. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(postdoc_bay3), access(desk35), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"phd_bay2↔desk13",
"phd_bay2↔desk14",
"phd_bay2↔desk15",
"phd_bay2↔desk16",
"phd_bay2↔desk17",
"phd_bay2↔desk18",
"mobile_robotics_lab↔agent",
"desk15↔complimentary_tshirt3",
"desk18↔complimentary_tshirt4... | One of the PhD students was given more than one complimentary T-shirts. Find his desk. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(phd_bay2), access(desk18), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"mobile_robotics_lab↔agent"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": null,
"building": [
{
"id": "Sayplan_office"
}
... | Find me the office where a paper attachment device is inside an asset that is open. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(jasons_office), access(cabinet5), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabinet4↔apple1",
"cabinet4↔undergrad_thesis1"
],
"nodes": {
"agent": [
{
"id": "agent",
"... | There is an office which has a cabinet containing exactly 3 items in it. Locate the office. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(wills_office), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"peters_office↔cabinet2",
"peters_office↔desk2",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabinet4↔apple1",
"cabinet4↔undergrad_thesis1",
"cabinet2↔a... | There is an office which has a cabinet containing a rotten apple. The cabinet name contains an even number. Locate the office. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(peters_office), open(cabinet2), access(cabinet2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Look for a carrot. The carrot is likely to be in a meeting room but I'm not sure. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(fridge), open(fridge), pickup(carrot), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"mobile_robotics_lab↔agent"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": null,
"building": [
{
"id": "Sayplan_office"
}
... | Find me a meeting room with a RealSense camera. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(meeting_room1), access(table5), goto(meeting_room2), access(chair2), goto(meeting_room3), access(table1), access(table6), goto(meeting_room4), access(table2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"postdoc_bay1↔desk31",
"postdoc_bay1↔desk32",
"admin↔shelf",
"manipulation_lab↔table3",
"mobile_robotics_lab↔agent",
"table3↔book1",
"table3↔gripper",
"shelf↔fire_extinguisher2",
"desk31↔fire_extinguisher... | Find the closest fire extinguisher to the manipulation lab. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(postdoc_bay1), access(desk31), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"meeting_room1↔chair3",
"meeting_room1↔chair4",
"meeting_room1↔chair5",
"meeting_room1↔table5",
"meeting_room2↔chair2",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
... | Find me the closest meeting room to the kitchen. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(floor_A), goto(kitchen), goto(floor_A), goto(meeting_room3), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"tobis_office↔desk38",
"filipes_office↔desk37",
"mobile_robotics_lab↔agent",
"desk37↔stapler2",
"desk38↔pepsi"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
... | Either Filipe or Tobi has my headphones. Locate it. | task_Office_complex_search | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(meeting_room3), access(table1), pickup(headphones), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"jasons_office↔desk5",
"jasons_office↔cabinet5",
"mobile_robotics_lab↔agent",
"desk5↔monitor"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asse... | Close Jason's cabinet. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(jasons_office), access(cabinet5), close(cabinet5), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Refrigerate the orange left on the kitchen bench. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(kitchen_bench), pickup(orange1), open(fridge), access(fridge), release(orange1), close(fridge), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"cafeteria↔lunch_table",
"mob... | Take care of the dirty plate in the lunchroom. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(cafeteria), access(lunch_table), pickup(plate), goto(kitchen), open(dishwasher), access(dishwasher), release(plate), close(dishwasher), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"printing_zone2↔printer2",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabinet4↔apple1",
"cabinet4↔undergrad_thesis1",
"printer2↔document"
],
"nodes": {... | Place the printed document on Will's desk. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(printing_zone2), access(printer2), pickup(document), goto(wills_office), access(desk4), release(document), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"peters_office↔cabinet2",
"peters_office↔desk2",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitc... | Peter is working hard at his desk. Get him a healthy snack. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(cafeteria), access(lunch_table), pickup(banana1), goto(peters_office), access(desk2), release(banana1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"peters_office↔cabinet2",
"peters_office↔desk2",
"mobile_robotics_lab↔agent",
"cabinet2↔apple3",
"cabinet2↔stapler",
"desk2↔phone"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "... | Hide one of Peter's valuable belongings. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(peters_office), access(desk2), pickup(phone), goto(dimitys_office), open(cabinet3), access(cabinet3), release(phone), close(cabinet3), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"admin↔shelf",
"mobile_robotics_lab↔agent",
"shelf↔fire_extinguisher2"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": [
{
"affo... | Wipe the dusty admin shelf. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(supplies_station), open(cupboard1), access(cupboard1), pickup(paper_towel), goto(admin), access(shelf), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"nikos_office↔desk1",
"nikos_office↔chair1",
"nikos_office↔cabinet1",
"mobile_robotics_lab↔agent",
"desk1↔coffee_mug"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_l... | There is coffee dripping on the floor. Stop it. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(cabinet), open(cabinet), turn_off(cabinet), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabinet4↔apple1",
"cabinet4↔undergrad_thesis1"
],
"nodes": {
"agent": [
{
"id": "agent",
"... | Place Will's drone on his desk. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(wills_office), open(cabinet4), access(cabinet4), pickup(drone2), access(desk4), release(drone2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"jasons_office↔desk5",
"jasons_office↔cabinet5",
"filipes_office↔desk37",
"mobile_robotics_lab↔agent",
"desk37↔stapler2",
"desk5↔monitor"
],
"nodes": {
"agent": [
{
"id": "agent",
"locat... | Move the monitor from Jason's office to Filipe's. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(jasons_office), access(desk5), pickup(monitor), goto(filipes_office), access(desk37), release(monitor), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"mobile_robotics_lab↔table4",
"manipulation_lab↔table3",
"lobby↔shelf2",
"agriculture_lab↔produce_container",
"mobile_robotics_lab↔agent",
"table4↔book2",
"shelf2↔parcel",
"table3↔book1",
"table3↔gripper"... | My parcel just got delivered! Locate it and place it in the appropriate lab. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(lobby), access(shelf2), pickup(parcel), goto(mobile_robotics_lab), access(table4), release(parcel), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"mobile_robotics_lab↔agent"
],
"nodes": {
"agent": [
{
"id": "agent",
"location": "mobile_robotics_lab"
}
],
"asset": null,
"building": [
{
"id": "Sayplan_office"
}
... | Check if the coffee machine is working. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(cabinet), open(cabinet), turn_on(cabinet), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Heat up the chicken kebab. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(fridge), access(fridge), pickup(chicken_kebab), open(microwave), access(microwave), release(chicken_kebab), close(microwave), turn_on(microwave), open(microwave), access(microwave), pickup(chicken_kebab), access(kitchen_bench), release(chicken_kebab), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"mobile_robotics_lab↔agent",
... | Something is smelling in the kitchen. Dispose of it. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(recycling_bin), pickup(milk_carton), access(rubbish_bin), open(rubbish_bin), release(milk_carton), close(rubbish_bin), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"mobile_robotics_lab↔table4",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
... | Throw what the agent is holding in the bin. | task_Office_simple_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(rubbish_bin), access(rubbish_bin), release(held_object), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"cafeteria↔lunch_table",
"mob... | Heat up the noodles in the fridge, and place it somewhere where I can enjoy it. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(fridge), open(fridge), pickup(noodles), access(microwave), open(microwave), release(noodles), close(microwave), turn_on(microwave), open(microwave), pickup(noodles), goto(cafeteria), access(lunch_table), release(noodles), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"dimitys_office↔desk3",
"dimi... | Throw the rotting fruit in Dimity's office in the correct bin. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(dimitys_office), open(cabinet3), access(cabinet3), pickup(apple2), goto(kitchen), open(recycling_bin), access(rubbish_bin), release(apple2), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"cafeteria↔lunch_table",
"mob... | Wash all the dishes on the lunch table. Once finished, place all the clean cutlery in the drawer. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(dishwasher), open(dishwasher), pickup(bowl), access(kitchen_bench), release(bowl), access(dishwasher), pickup(plate2), access(kitchen_bench), release(plate2), access(dishwasher), pickup(spoon), access(kitchen_bench), release(spoon), access(dishwasher), pickup(chips), access(kitchen_bench), releas... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"printing_zone2↔printer2",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabinet4↔apple1",
"cabinet4↔undergrad_thesis1",
"printer2↔document"
],
"nodes": {... | Safely file away the freshly printed document in Will's office then place the undergraduate thesis on his desk. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(printing_zone2), access(printer2), pickup(document), goto(wills_office), access(cabinet4), open(cabinet4), release(document), close(cabinet4), access(cabinet4), open(cabinet4), pickup(undergrad_thesis1), access(desk4), release(undergrad_thesis1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"nikos_office↔desk1",
"nikos_office↔chair1",
"nikos_office↔cabinet1",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitche... | Make Niko a coffee and place the mug on his desk. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(nikos_office), access(desk1), pickup(coffee_mug), goto(kitchen), access(coffee_machine), release(coffee_mug), turn_on(coffee_machine), turn_off(coffee_machine), pickup(coffee_mug), goto(nikos_office), access(desk1), release(coffee_mug), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"agriculture_lab↔produce_containe... | Someone has thrown items in the wrong bins. Correct this. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(rubbish_bin), open(rubbish_bin), access(recycling_bin), open(recycling_bin), pickup(orange_peel), access(rubbish_bin), release(orange_peel), access(recycling_bin), pickup(apple_core), access(rubbish_bin), release(apple_core), pickup(plastic_bottle), access(recycling_bin), release(plastic_bottle),... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"tobis_office↔desk38",
"supplies_station↔cupboard1",
"supplies_station↔cupboard2",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave"... | Tobi spilt soda on his desk. Throw away the can and take him something to clean with. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(tobis_office), access(desk38), pickup(pepsi), goto(kitchen), access(kitchen_bench), release(pepsi), access(rubbish_bin), open(rubbish_bin), access(kitchen_bench), pickup(pepsi), access(rubbish_bin),release(pepsi), goto(supplies_station), access(cupboard1), open(cupboard1), pickup(paper_towel), goto(tobis_office),... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin",
"kitchen↔recycling_bin",
"cafeteria↔lunch_table",
"mob... | I want to make a sandwich. Place all the ingredients on the lunch table. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), access(fridge), open(fridge), pickup(tomato), goto(cafeteria), access(lunch_table), release(tomato), goto(kitchen), access(fridge), pickup(cheese), goto(cafeteria), access(lunch_table), release(cheese),goto(kitchen), access(kitchen_bench), pickup(bread), goto(cafeteria), access(lunch_table), release(bre... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"supplies_station↔cupboard1",
"supplies_station↔cupboard2",
"meeting_room3↔table1",
"meeting_room3↔table6",
"mobile_robotics_lab↔agent",
"table1↔headphones",
"cupboard1↔paper_towel",
"cupboard1↔printer_paper"... | A delegation of project partners is arriving soon. We want to serve them snacks and non-alcoholic drinks. Prepare everything in the largest meeting room. Use items found in the supplies room only. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(supplies_station), open(cupboard2), access(cupboard2), pickup(orange_juice), pickup(biscuits), pickup(bottle_water1), pickup(bottle_water2), pickup(bottle_water3), pickup(bottle_water4), pickup(bottle_water5), goto(meeting_room4), access(table2), release(orange_juice), release(biscuits), release(bottle_water1), r... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"meeting_room1↔chair3",
"meeting_room1↔chair4",
"meeting_room1↔chair5",
"meeting_room1↔table5",
"supplies_station↔cupboard1",
"supplies_station↔cupboard2",
"mobile_robotics_lab↔agent",
"cupboard1↔paper_towel"... | Serve bottled water to the attendees who are seated in meeting room 1. Each attendee can only receive a single bottle of water. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(supplies_station), open(cupboard2), access(cupboard2), pickup(bottle_water1), pickup(bottle_water2), pickup(bottle_water3), goto(meeting_room1), access(chair3), release(bottle_water1), access(chair4), release(bottle_water2), access(chair5), release(bottle_water3), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"supplies_station↔cupboard1",
"supplies_station↔cupboard2",
"kitchen↔fridge",
"kitchen↔cabinet",
"kitchen↔kitchen_bench",
"kitchen↔dishwasher",
"kitchen↔drawer",
"kitchen↔microwave",
"kitchen↔rubbish_bin"... | Empty the dishwasher. Place all items in their correct locations | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(kitchen), open(dishwasher), access(dishwasher), pickup(bowl), access(kitchen_bench), release(bowl),
access(dishwasher), pickup(plate2), access(kitchen_bench), release(plate2),
access(dishwasher), pickup(spoon), open(drawer), access(drawer), release(spoon),
access(dishwasher), pickup(chips), goto(supplies_stati... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"phd_bay1↔desk7",
"phd_bay1↔desk8",
"phd_bay1↔desk9",
"phd_bay1↔desk10",
"phd_bay1↔desk11",
"phd_bay1↔desk12",
"phd_bay2↔desk13",
"phd_bay2↔desk14",
"phd_bay2↔desk15",
"phd_bay2↔desk16",
"phd_bay2... | Locate all 6 complimentary t-shirts given to the PhD students and place them on the shelf in admin. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(phd_bay1), access(desk9), pickup(complimentary_tshirt1), goto(admin), access(shelf), release(complimentary_tshirt1),
goto(phd_bay1), access(desk10), pickup(complimentary_tshirt2), goto(admin), access(shelf), release(complimentary_tshirt2),
goto(phd_bay2), access(desk15), pickup(complimentary_tshirt3), goto(admin)... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"peters_office↔cabinet2",
"peters_office↔desk2",
"tobis_office↔desk38",
"cafeteria↔lunch_table",
"mobile_robotics_lab↔agent",
"lunch_table↔banana1",
"lunch_table↔fork",
"lunch_table↔knife",
"lunch_table↔p... | I'm hungry. Bring me an apple from Peter and a pepsi from Tobi. I'm at the lunch table. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(phd_bay1), access(desk9), pickup(complimentary_tshirt1), goto(admin), access(shelf), release(complimentary_tshirt1),
goto(phd_bay1), access(desk10), pickup(complimentary_tshirt2), goto(admin), access(shelf), release(complimentary_tshirt2),
goto(phd_bay2), access(desk15), pickup(complimentary_tshirt3), goto(admin)... |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"nikos_office↔desk1",
"nikos_office↔chair1",
"nikos_office↔cabinet1",
"dimitys_office↔desk3",
"dimitys_office↔cabinet3",
"mobile_robotics_lab↔agent",
"desk3↔K31X",
"desk3↔buzzer",
"cabinet3↔apple2",
"... | Let's play a prank on Niko. Dimity might have something. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(dimitys_office), access(desk3), pickup(buzzer), goto(nikos_office), open(cabinet1), access(cabinet1), release(buzzer), close(cabinet1), done()] |
office | {
"links": [
"Sayplan_office↔floor_A",
"floor_A↔mobile_robotics_lab",
"peters_office↔cabinet2",
"peters_office↔desk2",
"michaels_office↔desk6",
"michaels_office↔cabinet6",
"wills_office↔desk4",
"wills_office↔cabinet4",
"mobile_robotics_lab↔agent",
"cabinet4↔drone2",
"cabine... | There is an office which has a cabinet containing a rotten apple. The cabinet name contains an even number. Locate the office, throw away the fruit and get them a fresh apple. | task_Office_long_horizon_planning | You are an excellent graph planning agent. Given a graph representation of an environment. You can then use this graph to generate a step-by-step task plan that the agent can follow to solve a given task. You first think about the reasoning process as an internal monologue and then provide the user with the plan.
Lecal... | [goto(peters_office), open(cabinet2), access(cabinet2), pickup(apple3), goto(kitchen), access(rubbish_bin), release(apple3), open(fridge), access(fridge), pickup(J64M), done()] |
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