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.

The emerging wave of technology in human-centric devices such as smart phones, tablets, and other small wearable sensor modules facilitates pervasive systems and applications to be economically deployed on a large scale with human participation. To exploit such environment, data gathering and dissemination based on opportunistic contact times among humans is a fundamental requirement. To tackle the lack of contemporaneous end-to-end connectivity in Delay-tolerant Networks (DTNs), most current algorithms assess the probability of the contact times to gradually convey a message towards its destination. These contact-based approaches do not perform well when historical locations of nodes have mixture distribution. In this paper, we formulate routing problems in spatial and spatiotemporal domains as an online unsupervised learning problem given location data. The key insight is that nodes frequently appearing nearer the message destinations are regarded as possessing higher delivery probability even if they have low contact times. We show how to solve the formulated problems with two basic algorithms, Location-Mean and Location-Cluster, by estimating the means of historical locations to calculate delivery probability of nodes. To our best knowledge, this is the first work to tackle DTN routing problem using online unsupervised learning on geographical locations. In the context of human mobility, simulation results of the Location-Mean algorithm show that the online unsupervised learning approach given node locations achieves better routing performances in term of delivery ratio, latency, transmission cost, and computation efficiency compared to the contact-based approach.

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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.

Artificial curiosity tries to maximize learning progress. We apply this concept to a physical system. Our Katana robot arm curiously plays with wooden blocks, using vision, reaching, and grasping. It is intrinsically motivated to explore its world. As a by-product, it learns how to place blocks stably, and how to stack blocks.

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.

In this paper, we present a new approach to solving the distributed facility location problem using the recent modeling and computational methodology of factor graph and message-passing. We first formulate the problem as finding a valid network configuration that minimizes the overall cost. We then represent the problem using a factor graph, and derive simplified, localized, broadcast-based message-passing rules which can elect a near-optimal set of facility nodes in a few iterations. Simulation results for small-world network topologies show that the algorithm is able to achieve good convergence rate and approximation ratio, and scalable to the network size.

Context-aware middleware encompasses uniform abstractions and reliable services for common operations, supports for most of the tasks involved in dealing with context, and thus simplifying the development of context-aware applications. In this paper, we address some key issues of a middleware for context-aware ubiquitous computing, ranging from design considerations of a unified sensing framework, formal modeling and representation of the real world, pluggable reasoning engines for high-level contexts, and context delivery-runtime service composition mechanisms. Our implementation experience indicates that a comprehensive approach throughout the system layers results in a flexible and reusable middleware architecture.