Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid

Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR

Improvising Linguistic Style: Social and Affective Bases for Agent Personality

This paper introduces Linguistic Style Improvisation, a theory and set of algorithms for improvisation of spoken utterances by artificial agents, with applications to interactive story and dialogue systems. We argue that linguistic style is a key aspect of character, and show how speech act representations common in AI can provide abstract representations from which computer characters can improvise. We show that the mechanisms proposed introduce the possibility of socially oriented agents, meet the requirements that lifelike characters be believable, and satisfy particular criteria for improvisation proposed by Hayes-Roth.Comment: 10 pages, uses aaai.sty, lingmacros.sty, psfig.st

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Proposition structure in framed decision problems: A formal representation.

Framing effects, which may induce decision-makers to demonstrate preference description invariance violation for logically equivalent options varying in semantic emphasis, are an economically significant decision bias and an active area of research. Framing is an issue inter alia for the way in which options are presented in stated-choice studies where (often inadvertent) semantic emphasis may impact on preference responses. While research into both espoused preference effects and its cognitive substrate is highly active, interpretation and explanation of preference anomalies is beset by variation in the underlying structure of problems and latitude for decision-maker elaboration. A formal, general scheme for making transparent the parameter and proposition structure of framed decision stimuli is described. Interpretive and cognitive explanations for framing effects are reviewed. The formalism’s potential for describing extant, generating new stimulus tasks, detailing decision-maker task elaboration. The approach also provides a means of formalising stated-choice response stimuli and provides a metric of decision stimuli complexity. An immediate application is in the structuring of stated-choice test instruments

Utilizing Statistical Dialogue Act Processing in Verbmobil

In this paper, we present a statistical approach for dialogue act processing in the dialogue component of the speech-to-speech translation system \vm. Statistics in dialogue processing is used to predict follow-up dialogue acts. As an application example we show how it supports repair when unexpected dialogue states occur.Comment: 6 pages; compressed and uuencoded postscript file; to appear in ACL-9

Boosting Deep Open World Recognition by Clustering

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set. Since it is practically impossible to capture all possible semantic concepts present in the real world in a single training set, we need to break the closed world assumption, equipping our robot with the capability to act in an open world. To provide such ability, a robot vision system should be able to (i) identify whether an instance does not belong to the set of known categories (i.e. open set recognition), and (ii) extend its knowledge to learn new classes over time (i.e. incremental learning). In this work, we show how we can boost the performance of deep open world recognition algorithms by means of a new loss formulation enforcing a global to local clustering of class-specific features. In particular, a first loss term, i.e. global clustering, forces the network to map samples closer to the class centroid they belong to while the second one, local clustering, shapes the representation space in such a way that samples of the same class get closer in the representation space while pushing away neighbours belonging to other classes. Moreover, we propose a strategy to learn class-specific rejection thresholds, instead of heuristically estimating a single global threshold, as in previous works. Experiments on RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202