Communicating with Teams of Cooperative Robots

Perzanowski, Dennis; Schultz, Alan C.; Adams, William; Bugajska, Magdalena D.; Marsh, Elaine; Trafton, J. Gregory; Brock, Derek; Skubic, Marjorie; Abramson, Myriam

doi:10.1007/978-94-017-2376-3_20

Cited by 17 publications

(11 citation statements)

References 3 publications

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“…The human user could interact with the mobile robot using natural language and gestures that are part of our multimodal interface [48]- [51], [55]. The natural language component of the interface uses a commercial speech recognition engine, ViaVoice, to analyze spoken utterances.…”

Section: Methodsmentioning

confidence: 99%

Enabling Effective Human–Robot Interaction Using Perspective-Taking in Robots

Trafton

Cassimatis

Bugajska

et al. 2005

IEEE Trans. Syst., Man, Cybern. A

242

123

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Abstract-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.

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Section: Methodsmentioning

confidence: 99%

Enabling Effective Human–Robot Interaction Using Perspective-Taking in Robots

Trafton

Cassimatis

Bugajska

et al. 2005

IEEE Trans. Syst., Man, Cybern. A

242

123

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show abstract

“…As we argued elsewhere [21], natural language provides an intuitively appropriate mode for interaction. We, therefore, incorporate speech to provide human users a natural way of interacting with the robot.…”

Section: Natural Language Understandingmentioning

confidence: 87%

“…This research is based on our previous work on natural language understanding, gesture recognition, and spatial reasoning [21,24] and can be compared with other research efforts; however, we differ from them in several ways. For example, while Leonardo [7] is designed as a stationary platform, our research focuses on a mobile one, placing restrictions on processing capabilities utilized for the task.…”

Section: Related Workmentioning

confidence: 99%

“…NAUTILUS, an in-house natural language understanding system [21], robustly parses and regularizes the utterances for further processing. These representations are combined with gesture information from the vision component to form context predicates.…”

Section: Natural Language Interactionsmentioning

confidence: 99%

“…Earlier shortcomings of the interface are discussed in the literature [21], but our main concern in integrating the various technologies here is to facilitate the disambiguation of referents in a humanrobot dialog, by using the strengths of the various modules in whatever combination seems most appropriate when sensor data or verbal input/output is insufficient to unambiguously identify either humans or objects in the environment. This paper describes the system and integration aspects of our work, as well as algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using vision, acoustics, and natural language for disambiguation

Fransen

Morariu

Martinson

et al. 2007

Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction

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Creating a human-robot interface is a daunting experience. Capabilities and functionalities of the interface are dependent on the robustness of many different sensor and input modalities. For example, object recognition poses problems for state-of-the-art vision systems. Speech recognition in noisy environments remains problematic for acoustic systems. Natural language understanding and dialog are often limited to specific domains and baffled by ambiguous or novel utterances. Plans based on domain-specific tasks limit the applicability of dialog managers. The types of sensors used limit spatial knowledge and understanding, and constrain cognitive issues, such as perspective-taking.In this research, we are integrating several modalities, such as vision, audition, and natural language understanding to leverage the existing strengths of each modality and overcome individual weaknesses. We are using visual, acoustic, and linguistic inputs in various combinations to solve such problems as the disambiguation of referents (objects in the environment), localization of human speakers, and determination of the source of utterances and appropriateness of responses when humans and robots interact. For this research, we limit our consideration to the interaction of two humans and one robot in a retrieval scenario. This paper will describe the system and integration of the various modules prior to future testing.

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Interactive Multi-Modal Robot Programming

Iba

Paredis

Khosla

2006

Springer Tracts in Advanced Robotics

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The goal of the Interactive Multi-Modal Robot Programming system is a comprehensive human-machine interface that allows non-experts to compose robot programs conveniently. Two key characteristics of this novel programming approach are that the user can provide feedback interactively at any time through an intuitive interface and that the system infers the user's intent to support interaction. The framework takes a three-step approach to the problem: multi-modal recognition, intention interpretation, and prioritized task execution. The system is demonstrated by interactively controlling and programming a mobile vacuum cleaning robot. The demonstrations are used to exemplify the interactive programming and plan recognition aspects of the research.

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Communicating with Teams of Cooperative Robots

Cited by 17 publications

References 3 publications

Enabling Effective Human–Robot Interaction Using Perspective-Taking in Robots

Enabling Effective Human–Robot Interaction Using Perspective-Taking in Robots

Using vision, acoustics, and natural language for disambiguation

Interactive Multi-Modal Robot Programming

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