Physical agents that can autonomously generate engaging, life-like behaviour will lead to more responsive and interesting robots and other autonomous systems. Although many advances have been made for one-to-one interactions in well controlled settings, future physical agents should be capable of interacting with humans in natural settings, including group interaction. In order to generate engaging behaviours, the autonomous system must first be able to estimate its human partners' engagement level. In this paper, we propose an approach for estimating engagement from behaviour and use the measure within a reinforcement learning framework to learn engaging interactive behaviours. 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 behaviours. Analysis based on sensory data and survey data shows that adaptable behaviours within a perceivable and understandable range can achieve higher engagement and likeability.
Interactions in natural settings, e.g., public spaces, are too complex to be simulated in controlled laboratory settings, so to understand natural HRI it is necessary to conduct field studies. Many human-robot interaction systems have been tested in public spaces such as hospitals @cite_18 , train stations @cite_4 , service points @cite_17 , airports @cite_31 , shopping malls @cite_12 , hotels @cite_5 and museums @cite_24 , among others. Although robots investigated in these papers are sometimes surrounded by a group of people in a public place, these robots typically interact with only one person at a specific time, i.e., one-to-one interaction. Unlike these works, the larger scale of LAS enables LAS to simultaneously accommodate interactions with multiple people. In addition, the behaviours of robots studied in most of these works are pre-designed by researchers without continuous learning and adaptability.
@cite_18 @cite_4 @cite_24 @cite_5 @cite_31 @cite_12 @cite_17
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Recent studies in human–robot interaction (HRI) have investigated ways to harness the power of the crowd for the purpose of creating robot interaction logic through games and teleoperation interfaces. Sensor networks capable of observing human–human interactions in the real world provide a potentially valuable and scalable source of interaction data that can be used for designing robot behavior. To that end, we present here a fully automated method for reproducing observed real-world social interactions with a robot. The proposed method includes techniques for characterizing the speech and locomotion observed in training interactions, using clustering to identify typical behavior elements and identifying spatial formations using established HRI proxemics models. Behavior logic is learned based on discretized actions captured from the sensor data stream, using a naive Bayesian classifier. Finally, we propose techniques for reproducing robot speech and locomotion behaviors in a robust way, despite the natural variation of human behaviors and the large amount of sensor noise present in speech recognition. We show our technique in use, training a robot to play the role of a shop clerk in a simple camera shop scenario, and we demonstrate through a comparison experiment that our techniques successfully enabled the generation of socially appropriate speech and locomotion behavior. Notably, the performance of our technique in terms of correct behavior selection was found to be higher than the success rate of speech recognition, indicating its robustness to sensor noise. In a typical Human-Robot Interaction (HRI) scenario, the robot needs to perform various tasks for the human, hence should take into account human oriented constraints. In this context it is not sufficient that the robot selects grasp and placement of the object from the stability point of view only. Motivated from human behavioral psychology, in this paper we emphasize on the mutually depended nature of grasp and placement selections, which is further constrained by the task, the environment and the human's perspective. We will explore essential human oriented constraints on grasp and placement selections and present a framework to incorporate them in synthesizing key configurations of planning basic interactive manipulation tasks. This paper presents a machine vision based ap- proach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i.e., colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs. I. INTRODUCTION Without the use of teleoperated and hand-held interaction devices, human operators generally face difficulties in select- ing and commanding individual and groups of robots from a relatively large group of spatially distributed robots (i.e., a swarm). However, due to the widespread availability of cost effective digital cameras onboard UGVs and UAVs, it is increasing the attention towards developing uninstrumented methods (i.e., methods that do not use sophisticated hardware devices from the human side) for human-swarm interaction (HSI). In previous work, we focused on learning efficient features incrementally (online) from multi-viewpoint images of multiple gestures that were acquired by a swarm of ground robots (1). In this paper, we present a cascaded supervised machine learning approach to deal with the machine vision problem of selecting 3D spatially-situated robots from a networked swarm based on the recognition of spatial hand gestures. These are a natural, easy recognizable, and device- less way to enable human operators to easily interact with external artifacts such as robots. Inspired by natural human behavior, we propose an ap- proach that combines face engagement and pointing gestures to interact with a swarm of robots: standing in front of a population of robots, by looking at them and pointing at them with spatial gestures, a human operator can designate individual or groups of robots of determined size. Robots cooperate to combine their independent observations of the human's face and gestures to cooperatively determine which robots were addressed (i.e., selected). While state of the art computer vision techniques pro- vide excellent face detection, human skeleton, and gesture recognition in ideal conditions, there are often occlusions, Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot's current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrated that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics.