麻豆视频

Sponsor: NSF

PI: Dan Szafir

Abstract:听Robots have the potential to significantly benefit society by actively collaborating with people in critical domains including manufacturing, healthcare, and space exploration. But to provide effective assistance, robots must be able to work with people in a natural, intuitive, and socially adept manner. To these ends, the PI will address the challenge of designing effective collaborative robots by developing a preliminary framework, process, and set of methods to sense and respond to implicit human communicative behaviors. This research will produce a set of generalizable design principles for collaborative robots, generate open-source algorithms showcasing practical implementations, and advance knowledge regarding computational understanding of human behaviors. It will synthesize theories of human communication and explore their application to human-robot interaction, as well as advancing knowledge regarding how robots might provide assistance as human collaborators and the types of sensors necessary for robots working closely with human partners. The PI's goal in this project is to establish a research program that will explore the design of effective behaviors for collaborative robots by developing computational models that enable them to sense implicit human communicative cues and guide robot responses by inferring cue intent, and to evaluate the effectiveness of the new algorithms in human-robot studies.

Publications

Connor Brooks and Daniel Szafir.听"Balanced Information Gathering and Goal-Oriented Actions in Shared Autonomy,"听ACM/IEEE International Conference on Human-Robot Interaction,听2019.听

Connor Brooks and Daniel Szafir.听"Building Second-Order Mental Models for Human-Robot Interaction,"听Association for the Advancement of Artificial Intelligence (AAAI) Fall Symposium Series on Artificial Intelligence for Human-Robot Interaction,听2019.

Connor Brooks, Madhur Atreya, and Daniel Szafir.听"Proactive Robot Assistants for Freeform Collaborative Tasks through Multimodal Recognition of Generic Subtasks,"听IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),听2018.

Daniel Prendergast and Daniel Szafir.听"Improving Object Disambiguation from Natural Language using Empirical Models,"听ACM International Conference on Multimodal Interaction (ICMI),听2018.

Michael Iuzzolino, Michael Walker, and Daniel Szafir.听"Virtual-to-Real-World Transfer Learning for Robot Navigation,"听IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),听2018.

VisuaLab

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