Synergy-based Hand Pose Sensing: Reconstruction Enhancement

Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware - hence basically at no extra cost - we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.Comment: Submitted to International Journal of Robotics Research (2012

Consensus Computation in Unreliable Networks: A System Theoretic Approach

This work addresses the problem of ensuring trustworthy computation in a linear consensus network. A solution to this problem is relevant for several tasks in multi-agent systems including motion coordination, clock synchronization, and cooperative estimation. In a linear consensus network, we allow for the presence of misbehaving agents, whose behavior deviate from the nominal consensus evolution. We model misbehaviors as unknown and unmeasurable inputs affecting the network, and we cast the misbehavior detection and identification problem into an unknown-input system theoretic framework. We consider two extreme cases of misbehaving agents, namely faulty (non-colluding) and malicious (Byzantine) agents. First, we characterize the set of inputs that allow misbehaving agents to affect the consensus network while remaining undetected and/or unidentified from certain observing agents. Second, we provide worst-case bounds for the number of concurrent faulty or malicious agents that can be detected and identified. Precisely, the consensus network needs to be 2k+1 (resp. k+1) connected for k malicious (resp. faulty) agents to be generically detectable and identifiable by every well behaving agent. Third, we quantify the effect of undetectable inputs on the final consensus value. Fourth, we design three algorithms to detect and identify misbehaving agents. The first and the second algorithm apply fault detection techniques, and affords complete detection and identification if global knowledge of the network is available to each agent, at a high computational cost. The third algorithm is designed to exploit the presence in the network of weakly interconnected subparts, and provides local detection and identification of misbehaving agents whose behavior deviates more than a threshold, which is quantified in terms of the interconnection structure

Synergy-Based Hand Pose Sensing: Optimal Glove Design

In this paper we study the problem of improving human hand pose sensing device performance by exploiting the knowledge on how humans most frequently use their hands in grasping tasks. In a companion paper we studied the problem of maximizing the reconstruction accuracy of the hand pose from partial and noisy data provided by any given pose sensing device (a sensorized "glove") taking into account statistical a priori information. In this paper we consider the dual problem of how to design pose sensing devices, i.e. how and where to place sensors on a glove, to get maximum information about the actual hand posture. We study the continuous case, whereas individual sensing elements in the glove measure a linear combination of joint angles, the discrete case, whereas each measure corresponds to a single joint angle, and the most general hybrid case, whereas both continuous and discrete sensing elements are available. The objective is to provide, for given a priori information and fixed number of measurements, the optimal design minimizing in average the reconstruction error. Solutions relying on the geometrical synergy definition as well as gradient flow-based techniques are provided. Simulations of reconstruction performance show the effectiveness of the proposed optimal design.Comment: Submitted to International Journal of Robotics Research 201

Communities of practice and what they can do for International Relations

This article argues that communities of practice (CoPs) provide IR with a unique way to understand how a small group of committed people can make a difference to international politics. The point is addressed in three steps. First, the article advances our understanding of how CoPs work. While at its core a CoP is a group of people brought together by a practice they enjoy, a CoP also shares a sense of timing, placing, and humour. These aspects help the group anchor, refine, and innovate their practice in the face of challenges and uncertainty. Second, the article contrasts the analysis of CoPs with other IR approaches, especially institutional analysis, network analysis, and epistemic communities, to show how CoPs supplement them. Third, the article illustrates the argument with the example of the EU foreign policy towards the Israeli-Palestinian conflict. It concludes by suggesting that a CoP's perspective not only helps IR better understand informal politics, but also opens up conversations across disciplines

On the Robust Synthesis of Logical Consensus Algorithms for Distributed Intrusion Detection

We introduce a novel consensus mechanism by which the agents of a network can reach an agreement on the value of a shared logical vector function depending on binary input events. Based on results on the convergence of finite-state iteration systems, we provide a technique to design logical consensus systems that minimizing the number of messages to be exchanged and the number of steps before consensus is reached, and tolerating a bounded number of failed or malicious agents. We provide sufficient joint conditions on the input visibility and the communication topology for the method’s applicability. We describe the application of our method to two distributed network intrusion detection problems

Social Robotics and Societies of Robots

The sustainability of social robotics, like other ambitious research programs, depends on the identification of lines of inquiry that are coherent with its visionary goals while satisfying more stringent constraints of feasibility and near-term payoffs. Within these constraints, this article outlines one line of inquiry that seems especially viable: development of a society of robots operating within the physical environments of everyday human life, developing rich robot–robot social exchanges, and yet, refraining from any physical contact with human beings. To pursue this line of inquiry effectively, sustained interactions between specialized research communities in robotics are needed. Notably, suitable robotic hand design and control principles must be adopted to achieve proper robotic manipulation of objects designed for human hands that one finds in human habitats. The Pisa-IIT SoftHand project promises to meet these manipulation needs by a principled combination of sensorimotor synergies and soft robotics actuation, which aims at capturing how the biomechanical structure and neural control strategies of the human hand interact so as to simplify and solve both control and sensing problems