A Unified Framework for Robots that Influence Humans over Long-Term Interaction Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2503.14633
Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human's behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge; by proactively nudging into the human's lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them. Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot's behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot's current choices will impact the human's future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments here: https://youtu.be/nPekTUfUEbo
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.14633
- https://arxiv.org/pdf/2503.14633
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414901209
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414901209Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2503.14633Digital Object Identifier
- Title
-
A Unified Framework for Robots that Influence Humans over Long-Term InteractionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-03-18Full publication date if available
- Authors
-
Shahabedin Sagheb, Sagar Parekh, Ravi Pandya, Ye-Ji Mun, Katherine Driggs-Campbell, Andrea Bajcsy, Dylan P. LoseyList of authors in order
- Landing page
-
https://arxiv.org/abs/2503.14633Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2503.14633Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2503.14633Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
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