English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

When humans interact with robots influence is inevitable. Consider anautonomous car driving near a human: the speed and steering of the autonomouscar will affect how the human drives. Prior works have developed frameworksthat enable robots to influence humans towards desired behaviors. But whilethese approaches are effective in the short-term (i.e., the first fewhuman-robot interactions), here we explore long-term influence (i.e., repeatedinteractions between the same human and robot). Our central insight is thathumans are dynamic: people adapt to robots, and behaviors which are influentialnow may fall short once the human learns to anticipate the robot's actions.With this insight, we experimentally demonstrate that a prevalentgame-theoretic formalism for generating influential robot behaviors becomesless effective over repeated interactions. Next, we propose three modificationsto Stackelberg games that make the robot's policy both influential andunpredictable. We finally test these modifications across simulations and userstudies: our results suggest that robots which purposely make their actionsharder to anticipate are better able to maintain influence over long-terminteraction. See videos here: https://youtu.be/ydO83cgjZ2Q

Towards Robots that Influence Humans over Long-Term Interaction