Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction Article Swipe
Ravi Pandya
,
Zhuoyuan Wang
,
Yorie Nakahira
,
Changliu Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.11893
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.11893
We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot's behavior, but such models do not account for the influence the robot has on the human's actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot's behavior based on a conditional model of the human that depends on the robot's future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human's intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users' interactions with a robot collaborator quantitatively and qualitatively.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.11893
- https://arxiv.org/pdf/2311.11893
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388926327
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388926327Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.11893Digital Object Identifier
- Title
-
Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-20Full publication date if available
- Authors
-
Ravi Pandya, Zhuoyuan Wang, Yorie Nakahira, Changliu LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.11893Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.11893Direct 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/2311.11893Direct OA link when available
- Concepts
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Robot, Computer science, Artificial intelligence, Task (project management), Human–robot interaction, Observer (physics), Human–computer interaction, Machine learning, Plan (archaeology), Focus (optics), Mobile robot, Engineering, History, Physics, Archaeology, Quantum mechanics, Optics, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.conditional | 90, 110, 137 |
| abstract_inverted_index.data-sparse | 128 |
| abstract_inverted_index.formulation | 108 |
| abstract_inverted_index.human-robot | 31 |
| abstract_inverted_index.inefficient | 73 |
| abstract_inverted_index.model-based | 107 |
| abstract_inverted_index.preexisting | 23 |
| abstract_inverted_index.collaborator | 175 |
| abstract_inverted_index.interactions | 171 |
| abstract_inverted_index.specifically | 19 |
| abstract_inverted_index.collaboration | 32 |
| abstract_inverted_index.collaborative | 84, 159 |
| abstract_inverted_index.environments. | 129 |
| abstract_inverted_index.interactions. | 74 |
| abstract_inverted_index.observational | 34 |
| abstract_inverted_index.collaborators. | 149 |
| abstract_inverted_index.qualitatively. | 178 |
| abstract_inverted_index.quantitatively | 176 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |