Learning and interacting in human-robot domains

Nicolescu, Mircea; Matarić, Maja J.

doi:10.1109/3468.952716

Cited by 151 publications

(96 citation statements)

References 15 publications

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“…Since most robots have fairly rudimentary capability to recognize and produce speech, non-verbal dialogue is a useful alternative. Nicolescu and Mataric, for example, describe a robot that asks humans for help, communicating its needs and intentions through its actions [169].…”

Section: Non-verbalmentioning

confidence: 99%

A survey of socially interactive robots

Fong

Nourbakhsh

Dautenhahn

2003

Robotics and Autonomous Systems

2,415

1,378

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This report reviews "socially interactive robots": robots for which social human-robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of "social robots". We then present a taxonomy of design methods and system components used to build socially interactive robots. Following this taxonomy, we survey the current state of the art, categorized by use and application area. Finally, we describe the impact of these these robots on humans and discuss open issues.

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Section: Non-verbalmentioning

confidence: 99%

A survey of socially interactive robots

Fong

Nourbakhsh

Dautenhahn

2003

Robotics and Autonomous Systems

2,415

1,378

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“…In addition, learning at different hierarchy levels has also been studied. Several researchers consider the imitation of the motion primitives themselves [9], [10], [8], while other works consider how a system may learn a sequence of primitives which are defined a-priori [14], [15]. Takano and Nakamura [16] and Kulić et al [17] consider the case where both the motion primitives and their sequencing is learned simultaneously.…”

Section: A Related Workmentioning

confidence: 99%

Incremental learning of human behaviors using hierarchical hidden Markov models

Kulić

Nakamura

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper proposes a novel approach for extracting a model of movement primitives and their sequential relationships during online observation of human motion. In the proposed approach, movement primitives, modeled as hidden Markov models, are autonomously segmented and learned incrementally during observation. At the same time, a higher abstraction level hidden Markov model is also learned, encapsulating the relationship between the movement primitives. For the higher level model, each hidden state represents a motion primitive, and the observation function is based on the likelihood that the observed data is generated by the motion primitive model. An approach for incremental training of the higher order model during online observation is developed. The approach is validated on a dataset of continuous movement data.

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“…LfD methods often rely on robot vision to track and follow the user during a demonstration [11]. Gestures are commonly applied to initiate, stop or switch between robot tasks, but also give insight into the intended user behavior.…”

Section: Relevant Workmentioning

confidence: 99%

User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots

et al. 2016

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Abstract. Social robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett's 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.

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Learning and interacting in human-robot domains

Cited by 151 publications

References 15 publications

A survey of socially interactive robots

A survey of socially interactive robots

Incremental learning of human behaviors using hierarchical hidden Markov models

User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots

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