Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction

Sara Sheikholeslami, Justin W. Hart, Wesley P. Chan, Camilo P. Quintero, Elizabeth A. Croft

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


In human-human interactions, individuals naturally achieve fluency by anticipating the partners actions. This predictive ability is largely lacking in robots, leading to stilted human-robot interactions. We aim to improve fluency in human-robot reaching motions using a unified predictive model of human reaching motions. Using this model, we allow the robot to infer human intent, while also applying the same model to generate the robots motion to make its intent more transparent to the human. We conducted a study on human reaching motion and constructed an elliptical motion model that is shown to yield a good fit to empirical data. In future studies, we plan to confirm the effectiveness of this model in predicting human intent and conveying robot intent for achieving fluency in human-robot handovers.

Original languageEnglish
Title of host publicationCompanion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
EditorsGuy Hoffman, Adriana Tapus
Place of PublicationNew York USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
Publication statusPublished - 1 Mar 2018
Externally publishedYes
EventAnnual ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2018 - Chicago, United States of America
Duration: 5 Mar 20188 Mar 2018
Conference number: 13th (Proceedings)


ConferenceAnnual ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2018
Abbreviated titleHRI 2018
CountryUnited States of America
Internet address

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