Learning how to increase the chance of human-robot engagement

Macharet, Douglas G.; Florêncio, Dinei

doi:10.1109/iros.2013.6696660

Cited by 18 publications

(6 citation statements)

References 31 publications

(29 reference statements)

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“…In one of the recent works, HRI is studied and considered for human-aware robot navigation, where traditional motion planning approach is amended to respect the effect of HRI on human behavior in the presence of mobile robot [1]. In addition to considering human being as moving obstacles, criteria such as human comfort [2,3], natural motion [4,5] and socially-adaptive motion [6,7] are taken in to account for robot motion planning.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian-Robot Interaction Experiments in an Exit Corridor

Chen

Jiang

Guo

2018

2018 15th International Conference on Ubiquitous Robots (UR)

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The study of human-robot interaction (HRI) has received increasing research attention for robot navigation in pedestrian crowds. In this paper, we present empirical study of pedestrian-robot interaction in an uni-directional exit corridor. We deploy a mobile robot moving in a direction perpendicular to that of the pedestrian flow, and install a pedestrian motion tracking system to record the collective motion. We analyze both individual and collective motion of pedestrians, and measure the effect of the robot motion on the overall pedestrian flow. The experimental results show the effect of passive HRI, where the pedestrians' overall speed is slowed down in the presence of the robot, and the faster the robot moves, the lower the average pedestrian velocity becomes. Experiment results show qualitative consistency of the collective HRI effect with simulation results that was previously reported. The study can be used to guide future design of robot-assisted pedestrian evacuation algorithms.

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Section: Introductionmentioning

confidence: 99%

Pedestrian-Robot Interaction Experiments in an Exit Corridor

Chen

Jiang

Guo

2018

2018 15th International Conference on Ubiquitous Robots (UR)

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“…The predicted affective state is then used to calculate the reward in an RL framework to personalize response to users. Macharet and Florencio [51] investigated the effectiveness of using RL to learn socially acceptable approaching behavior for a mobile robot. Gordon et al [27] studied affective personalization in an integrated intelligent tutoring system, where in a one-to-one interaction setting, facial expression based engagement and valence estimation are used as reward in a standard SARSA algorithm.…”

Section: :5mentioning

confidence: 99%

Learning to Engage with Interactive Systems

Meng

Lin

Francey

et al. 2020

J. Hum.-Robot Interact.

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Physical agents that can autonomously generate engaging, lifelike behavior will lead to more responsive and user-friendly robots and other autonomous systems. Although many advances have been made for one-toone interactions in well-controlled settings, physical agents should be capable of interacting with humans in natural settings, including group interaction. To generate engaging behaviors, the autonomous system must first be able to estimate its human partners' engagement level. In this article, we propose an approach for estimating engagement during group interaction by simultaneously taking into account active and passive interaction, and use the measure as the reward signal within a reinforcement learning framework to learn engaging interactive behaviors. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviors. Analysis based on sensory data and survey data shows that adaptable behaviors within an expert-designed action space can achieve higher engagement and likeability.

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“…Similar research used proxemics for initiating an interaction from close distance (e.g., [16]) and for drawing people's attention from public distance (e.g., [17]). Furthermore, the four proxemic zones defined by Hall [18] were shown to be applicable in HRI (e.g., [19], as cited in [20]).…”

Section: B Design Parametersmentioning

confidence: 99%

An Iterative Interaction-Design Method for Multi-Modal Robot Communication

Saad

Broekens

Neerincx

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task-and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her "own" individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters. We conducted a study with Industrial Design students (N = 13) who followed the ID-method and used our tool to design person-and task-oriented communications for a reception robot. Our method produced distinctive task-and person-oriented dialogue styles, i.e., provided the predicted theory-based multimodal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns.

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Learning how to increase the chance of human-robot engagement

Cited by 18 publications

References 31 publications

Pedestrian-Robot Interaction Experiments in an Exit Corridor

Pedestrian-Robot Interaction Experiments in an Exit Corridor

Learning to Engage with Interactive Systems

An Iterative Interaction-Design Method for Multi-Modal Robot Communication

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