TANGO: Commonsense Generalization in Predicting Tool Interactions for\n Mobile Manipulators Article Swipe
Shreshth Tuli
,
Rajas Bansal
,
Rohan Paul
,
Mausam Mausam
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.04556
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.04556
Robots assisting us in factories or homes must learn to make use of objects\nas tools to perform tasks, e.g., a tray for carrying objects. We consider the\nproblem of learning commonsense knowledge of when a tool may be useful and how\nits use may be composed with other tools to accomplish a high-level task\ninstructed by a human. We introduce a novel neural model, termed TANGO, for\npredicting task-specific tool interactions, trained using demonstrations from\nhuman teachers instructing a virtual robot. TANGO encodes the world state,\ncomprising objects and symbolic relationships between them, using a graph\nneural network. The model learns to attend over the scene using knowledge of\nthe goal and the action history, finally decoding the symbolic action to\nexecute. Crucially, we address generalization to unseen environments where some\nknown tools are missing, but alternative unseen tools are present. We show that\nby augmenting the representation of the environment with pre-trained embeddings\nderived from a knowledge-base, the model can generalize effectively to novel\nenvironments. Experimental results show a 60.5-78.9% absolute improvement over\nthe baseline in predicting successful symbolic plans in unseen settings for a\nsimulated mobile manipulator.\n
Related Topics
Concepts
Computer science
Generalization
Artificial intelligence
Task (project management)
Action (physics)
Human–computer interaction
Robot
Planner
Representation (politics)
Graph
Artificial neural network
Commonsense knowledge
Knowledge base
Machine learning
Theoretical computer science
Physics
Economics
Mathematics
Political science
Mathematical analysis
Politics
Law
Management
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2105.04556
- https://arxiv.org/pdf/2105.04556
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287185061
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287185061Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2105.04556Digital Object Identifier
- Title
-
TANGO: Commonsense Generalization in Predicting Tool Interactions for\n Mobile ManipulatorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-05Full publication date if available
- Authors
-
Shreshth Tuli, Rajas Bansal, Rohan Paul, Mausam MausamList of authors in order
- Landing page
-
https://arxiv.org/abs/2105.04556Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2105.04556Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2105.04556Direct OA link when available
- Concepts
-
Computer science, Generalization, Artificial intelligence, Task (project management), Action (physics), Human–computer interaction, Robot, Planner, Representation (politics), Graph, Artificial neural network, Commonsense knowledge, Knowledge base, Machine learning, Theoretical computer science, Physics, Economics, Mathematics, Political science, Mathematical analysis, Politics, Law, Management, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.absolute | 158 |
| abstract_inverted_index.baseline | 161 |
| abstract_inverted_index.carrying | 22 |
| abstract_inverted_index.composed | 43 |
| abstract_inverted_index.consider | 25 |
| abstract_inverted_index.decoding | 108 |
| abstract_inverted_index.history, | 106 |
| abstract_inverted_index.how\nits | 39 |
| abstract_inverted_index.learning | 28 |
| abstract_inverted_index.missing, | 124 |
| abstract_inverted_index.network. | 90 |
| abstract_inverted_index.objects. | 23 |
| abstract_inverted_index.present. | 130 |
| abstract_inverted_index.settings | 169 |
| abstract_inverted_index.symbolic | 83, 110, 165 |
| abstract_inverted_index.teachers | 71 |
| abstract_inverted_index.that\nby | 133 |
| abstract_inverted_index.assisting | 1 |
| abstract_inverted_index.factories | 4 |
| abstract_inverted_index.introduce | 56 |
| abstract_inverted_index.knowledge | 30, 100 |
| abstract_inverted_index.over\nthe | 160 |
| abstract_inverted_index.60.5-78.9% | 157 |
| abstract_inverted_index.Crucially, | 113 |
| abstract_inverted_index.accomplish | 48 |
| abstract_inverted_index.augmenting | 134 |
| abstract_inverted_index.generalize | 149 |
| abstract_inverted_index.high-level | 50 |
| abstract_inverted_index.predicting | 163 |
| abstract_inverted_index.successful | 164 |
| abstract_inverted_index.alternative | 126 |
| abstract_inverted_index.commonsense | 29 |
| abstract_inverted_index.effectively | 150 |
| abstract_inverted_index.environment | 139 |
| abstract_inverted_index.from\nhuman | 70 |
| abstract_inverted_index.improvement | 159 |
| abstract_inverted_index.instructing | 72 |
| abstract_inverted_index.objects\nas | 13 |
| abstract_inverted_index.pre-trained | 141 |
| abstract_inverted_index.some\nknown | 121 |
| abstract_inverted_index.Experimental | 153 |
| abstract_inverted_index.a\nsimulated | 171 |
| abstract_inverted_index.environments | 119 |
| abstract_inverted_index.the\nproblem | 26 |
| abstract_inverted_index.to\nexecute. | 112 |
| abstract_inverted_index.graph\nneural | 89 |
| abstract_inverted_index.interactions, | 66 |
| abstract_inverted_index.relationships | 84 |
| abstract_inverted_index.task-specific | 64 |
| abstract_inverted_index.demonstrations | 69 |
| abstract_inverted_index.generalization | 116 |
| abstract_inverted_index.manipulator.\n | 173 |
| abstract_inverted_index.representation | 136 |
| abstract_inverted_index.for\npredicting | 63 |
| abstract_inverted_index.knowledge-base, | 145 |
| abstract_inverted_index.task\ninstructed | 51 |
| abstract_inverted_index.state,\ncomprising | 80 |
| abstract_inverted_index.embeddings\nderived | 142 |
| abstract_inverted_index.novel\nenvironments. | 152 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.2170922 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |