Spinning particles and higher spin fields on (A)dS backgrounds

Spinning particle models can be used to describe higher spin fields in first quantization. In this paper we discuss how spinning particles with gauged O(N) supersymmetries on the worldline can be consistently coupled to conformally flat spacetimes, both at the classical and at the quantum level. In particular, we consider canonical quantization on flat and on (A)dS backgrounds, and discuss in detail how the constraints due to the worldline gauge symmetries produce geometrical equations for higher spin fields, i.e. equations written in terms of generalized curvatures. On flat space the algebra of constraints is linear, and one can integrate part of the constraints by introducing gauge potentials. This way the equivalence of the geometrical formulation with the standard formulation in terms of gauge potentials is made manifest. On (A)dS backgrounds the algebra of constraints becomes quadratic, nevertheless one can use it to extend much of the previous analysis to this case. In particular, we derive general formulas for expressing the curvatures in terms of gauge potentials and discuss explicitly the cases of spin 2, 3 and 4.Comment: 35 pages, added reference

Teaching robots parametrized executable plans through spoken interaction

While operating in domestic environments, robots will necessarily face difficulties not envisioned by their developers at programming time. Moreover, the tasks to be performed by a robot will often have to be specialized and/or adapted to the needs of specific users and specific environments. Hence, learning how to operate by interacting with the user seems a key enabling feature to support the introduction of robots in everyday environments. In this paper we contribute a novel approach for learning, through the interaction with the user, task descriptions that are defined as a combination of primitive actions. The proposed approach makes a significant step forward by making task descriptions parametric with respect to domain specific semantic categories. Moreover, by mapping the task representation into a task representation language, we are able to express complex execution paradigms and to revise the learned tasks in a high-level fashion. The approach is evaluated in multiple practical applications with a service robot

Massive and massless higher spinning particles in odd dimensions

We study actions for massive bosonic particles of higher spins by dimensionally reducing an action for massless particles. For the latter we take a model with a SO(N) extended local supersymmetry on the worldline, that is known to describe massless (conformal) particles of higher spins in flat spacetimes of even dimensions. Dimensional reduction produces an action for massive spinning particles in odd dimensions. The field equations that emerge in a quantization a la Dirac are shown to be equivalent to the Fierz-Pauli ones. The massless limit generates a multiplet of massless states with higher spins, whose first quantized field equations have a geometric form with fields belonging to various types of Young tableaux. These geometric equations can be partially integrated to show their equivalence with the standard Fronsdal-Labastida equations. We covariantize our model to check whether an extension to curved spacetimes can be achieved. Restricting to (A)dS spaces, we find that the worldline gauge algebra becomes nonlinear, but remains first class. This guarantees consistency on such backgrounds. A light cone analysis confirms the presence of the expected propagating degrees of freedom. A covariant analysis is worked out explicitly for the massive case, which is seen to give rise to the Fierz-Pauli equations extended to (A)dS spaces. It is worth noting that in D=3 the massless limit of our model when N goes to infinity has the same field content of the Vasiliev's theory that accommodates each spin exactly once.Comment: 31 page

Update of time-invalid information in Knowledge Bases through Mobile Agents

In this paper, we investigate the use of a mobile, autonomous agent to update knowledge bases containing statements that lose validity with time. This constitutes a key issue in terms of knowledge acquisition and representation, because dynamic data need to be constantly re-evaluated to allow reasoning. We focus on the way to represent the time- validity of statements in a knowledge base, and on the use of a mobile agent to update time-invalid statements while planning for “information freshness” as the main objective. We propose to use Semantic Web standards, namely the RDF model and the SPARQL query language, to represent time-validity of information and decide how long this will be considered valid. Using such a representation, a plan is created for the agent to update the knowledge, focusing mostly on guaranteeing the time-validity of the information collected. To show the feasibility of our approach and discuss its limitations, we test its implementation on scenarios in the working environment of our research lab, where an autonomous robot is used to sense temperature, humidity, wifi signal and number of people on demand, updating the knowledge base with time- valid information

Particles with non abelian charges

Efficient methods for describing non abelian charges in worldline approaches to QFT are useful to simplify calculations and address structural properties, as for example color/kinematics relations. Here we analyze in detail a method for treating arbitrary non abelian charges. We use Grassmann variables to take into account color degrees of freedom, which however are known to produce reducible representations of the color group. Then we couple them to a U(1) gauge field defined on the worldline, together with a Chern-Simons term, to achieve projection on an irreducible representation. Upon gauge fixing there remains a modulus, an angle parametrizing the U(1) Wilson loop, whose dependence is taken into account exactly in the propagator of the Grassmann variables. We test the method in simple examples, the scalar and spin 1/2 contribution to the gluon self energy, and suggest that it might simplify the analysis of more involved amplitudes.Comment: 14 page

DKA-robo: dynamically updating time-invalid knowledge bases using robots

In this paper we present the DKA-robo framework, where a mobile agent is used to update those statements of a knowledge base that have lost validity in time. Managing the dynamic information of knowledge bases constitutes a key issue in many real-world scenarios, because constantly reevaluating data requires efforts in terms of knowledge acquisition and representation. Our solution to such a problem is to use RDF and SPARQL to represent and manage the time-validity of information, combined with an agent acting as a mobile sensor which updates the outdated statements in the knowledge base, therefore always guaranteeing time-valid results against user queries. This demo shows the implementation of our approach in the working environment of our research lab, where a robot is used to sense temperature, humidity, wifi- signal and number of people on demand, updating the lab knowledge base with time-valid information

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS