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()] |
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