Human-swarm interaction (HSI) is a developing field of research in which the problem of gesture-based control has been attracting an increasing attention, being at the same time a natural form of interaction and an effective way to point and select individual or groups of robots in the swarm. Gesture-based interaction usually requires vision-based recognition and classification of the gesture from the swarm. At this aim, existing methods for cooperative sensing and recognition make use of distributed consensus algorithms, which include for instance averaging and frequency counting. In this work we present a distributed consensus protocol that allows robot swarms to learn efficiently gestures from online interactions with a human teacher. The protocol also facilitates the integration of different consensus algorithms. Experiments have been performed in emulation using on real data acquired by a swarm of robots. The results indicate that effectively exploiting the collective decision-making of the swarm is a viable way to rapidly achieve good learning performance.

As a first step towards human and multiple-UAV interaction, we present a novel method for humans to interact with flying UAVs using locally on-board video cameras. Using machine vision techniques, our approach enables human operators to command and control Parrot drones by giving them directions to move, using simple hand gestures. When a direction to move is given, the robot controller estimates the angle and distance to move with the help of a face score system and the estimated hand direction. This approach offers mobile robots the ability localize with human operators and provides UAVs/UGVs with a better perception of the environment around the human.

No abstract available.

Human localization is fundamental in human centered computing and human-robot interaction (HRI), as human operators should be localized by robots before being actively serviced. This paper proposes a simple and efficient approach for estimating the distance and orientation of an human, from a single robot-acquired image. We adopt a simple combination of multiple Haar feature-based classifiers to compute face scores, that represent the probability that the detected face is acquired from each of a predefined set of poses. Using the Locally Weighted Projectron Regression (LWPR), an online incremental regression-based learning scheme, we can reliably learn and predict the pose of a human face in real-time at a low computational cost. The accuracy, robustness, and scalability of the obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.

We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. We address the problem of online feature learning by proposing Convolutional Max-Pooling (CMP), a simple feed-forward two-layer network derived from the deep hierarchical Max-Pooling Convolutional Neural Network (MPCNN). To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme. In order for all robots to collectively take part in the learning and recognition task and obtain a swarm-level classification, we build a distributed consensus by fusing the individual decision opinions of robots together with the individual weights generated from multiple classifiers. Accuracy, robustness, and scalability of obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.

This paper presents a machine vision based approach 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.

We introduce a novel algorithm called upper confidence - weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In UCB, each instance is classified using both score and uncertainty. For a given instance in the sequence, the algorithm might guess its class label primarily to reduce the class uncertainty. This is a form of informed exploration, which enables the performance to improve with lower sample complexity compared to the case without exploration. Combining UCB with SCW leads to the ability to deal well with noisy and nonseparable data, and state-of-the-art performance is achieved without increasing the computational cost. A potential application setting is human-robot interaction (HRI), where the robot is learning to classify some set of inputs while the human teaches it by providing only binary feedback—or sometimes even the wrong answer entirely. Experimental results in the HRI setting and with two benchmark datasets from other settings show that UCWL outperforms other state-of-the-art algorithms in the online binary feedback setting—and surprisingly even sometimes outperforms state-of-the-art algorithms that get full feedback (e.g., the true class label), whereas UCWL gets only binary feedback on the same data sequence.

No abstract available.

We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides the computational capability of online incremental learning and is robust for typical learning scenarios in which training samples arrive in mini-batches. This is the case for visual learning and recognition in multi-robot systems, where each robot acquires a different image of the same sample. The experimental results indicate that the CNSVM can be successfully applied to visual learning and recognition of hand gestures as well as to measure learning progress.

We present an approach for distributed real-time recognition tasks using a swarm of mobile robots. We focus on the visual recognition of hand gestures, but the solutions that we provide have general applicability and address a number of challenges common to many distributed sensing and classification problems. In our approach, robots acquire and process hand images from multiple points of view, most of which do not allow for a satisfactory classification. Each robot is equipped with a statistical classifier, which is used to generate an opinion for the sensed gesture. Using a low-bandwidth wireless channel, the robots locally exchange their opinions. They also exploit mobility to adapt their positions to maximize the mutual information collectively gathered by the swarm. A distributed consensus protocol is implemented, to allow to rapidly settle on a decision once enough evidence is available. The system is implemented and demonstrated on real robots. In addition, extensive quantitative results of emulation experiments, based on a real image dataset, are reported. We consider different scenarios and study the scalability and the robustness of the swarm performance for distributed recognition.

In this paper we consider a human-swarm interaction scenario based on hand gestures. We study how the swarm can incrementally learn hand gestures through the interaction with a human instructor providing training gestures and correction feedback. The main contribution of the paper is a novel incremental machine learning approach that makes the robot swarm learn and recognize the gestures in a distributed and decentralized fashion using binary (i.e., yes/no) feedback. It exploits cooperative information exchange and swarm's intrinsic parallelism and redundancy. We perform extensive tests using real gesture images, showing that good classification accuracies are obtained even with rather few training samples and relatively small swarms. We also show the good scalability of the approach and its relatively low requirements in terms of communication overhead.