J. G. Trafton

Enabling effective human-robot interaction using perspective-taking in robots

We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person.

A Preliminary Study of Peer-to-Peer Human-Robot Interaction

The Peer-To-Peer Human-Robot Interaction (P2P-HRI) project is developing techniques to improve task coordination and collaboration between human and robot partners. Our work is motivated by the need to develop effective human-robot teams for space mission operations. A central element of our approach is creating dialogue and interaction tools that enable humans and robots to flexibly support one another. In order to understand how this approach can influence task performance, we recently conducted a series of tests simulating a lunar construction task with a human-robot team. In this paper, we describe the tests performed, discuss our initial results, and analyze the effect of intervention on task performance.

Perspective-taking with robots: experiments and models

We suggest that to enable effective human-robot interaction, robots should be able to interact in a way that is natural to and preferred by humans. Using human-compatible representations and reasoning mechanisms should help in developing skills which support effective human-robot interaction. In this paper, we present two studies that examine a critical human-robot-interaction component: perspective-taking. We find that when a person asks a robot to perform a task with some ambiguity to the robot, the person prefers the robot to either ask for clarification or take the persons perspective and act appropriately.

Toward Multimodal Human-Robot Cooperation and Collaboration

*† ‡ § ** †† ‡‡ §§ Our multimodal interface integrates speech recognition, natural language understanding, spatial reasoning and human cognitive models for completing specific tasks and for perspective-taking in locative oriented tasks. With natural language and gestures, we believe human-robot interaction and communication is facilitated. Instead of concentrating on the various modalities of the interface, users can concentrate on the task at hand. Likewise, by incorporating human cognitive models for handling spatial information and perspective-taking, as well as for specific task completion, a better match with the expectations that humans acquire from their human-human interactions should be obtained, further facilitating cooperation and collaboration in human-robot interactions.