In this work we propose, on a conceptual level, a 2-D localization method based on Qualitative Spatial Reasoning (QSR). QSR is a formal way to reason about spatial entities in the environment and their relationship. We use QSR framework as observations that connect observer's position with the environment using a predefined map. These observations are, per definition, qualitative and do not contain quantitative numerical values. We develop a way of incorporating these observations in the Bayesian filter setting, mainly by creating a fitting likelihood function. The main motivation for this work comes from a case where a person wants to localize itself in the environment, but without any concrete quantitative measurements. We demonstrate the method with a small simulation example showing that it can produce a fairly good position estimate compared to the case without support from qualitative observations, i.e., pure dead-reckoning. The comparison is based on Monte Carlo evaluation. We also suggest future improvements of the method with extension to 3-D case, that can be useful for flying platforms, as well as the possibility of building the map concurrently with localization.

Actuator faults in dynamic systems pose significant challenges, particularly for robotic systems operating in hostile environments such as Autonomous Underwater Vehicles (AUVs), risking loss of stability and performance degradation. Fault Tolerant Control (FTC) strategies, including Control Reallocation (CR), have been developed to mitigate such risks. However, these strategies extensively depend on explicit fault diagnosis, which may present challenges regarding computational demands and efficiency, particularly when dealing with unknown faults. This paper presents a novel method that performs CR with Deep Reinforcement Learning (DRL) for actuator fault recovery without explicit fault diagnosis. The approach is implemented on a BlueROV2 underwater vehicle and demonstrates improved performance for fault recovery compared to a standard Proportional-Integral-Derivative (PID) controller and a variable gain PID controller, both in simulation and in real-world conditions. The DRL-based CR method demonstrates generalisability by successfully handling faults not encountered during training, highlighting its adaptability to unforeseen circumstances.

Emotion recognition is a topic of significant interest in assistive robotics due to the need to equip robots with the ability to comprehend human behavior, facilitating their effective interaction in our society. Consequently, efficient and dependable emotion recognition systems supporting optimal human-machine communication are required. Multi-modality (including speech, audio, text, images, and videos) is typically exploited in emotion recognition tasks. Much relevant research is based on merging multiple data modalities and training deep learning models utilizing low-level data representations. However, most existing emotion databases are not large (or complex) enough to allow machine learning approaches to learn detailed representations. This paper explores modality-specific pre-trained transformer frameworks for self-supervised learning of speech and text representations for data-efficient emotion recognition while achieving state-of-the-art performance in recognizing emotions. This model applies feature-level fusion using nonverbal cue data points from motion capture to provide multimodal speech emotion recognition. The model was trained using the publicly available IEMOCAP dataset, achieving an overall accuracy of 77.58% for four emotions, outperforming state-of-the-art approaches

Ambiguous or inconsistent information is frequently present in the input of any real-world system that relies on multiple data sources. In the presence of inconsistencies, an expert system based on classical logic is trivialised due to the principle of explosion, that forces any proposition (and its negation) to be inferred. Instead, a family of non-classical logics, called Paraconsistent Logics, formalises the idea that even from inconsistent premises, useful conclusions can be drawn. Within this context, the Paraconsistent Annotated Logics with Annotation of Two Values (PAL2v), which use a pair of favourable and unfavourable evidences as annotations, have been particularly successful in engineering applications. Structures called Para-Analysers can be built with these logics that provide the appropriate means to analyse the truth value of a proposition under conflicting/contradictory evidences. The work reported in this paper extends the traditional Para-Analysers with an evidence filter. This new model can be represented as a cubic structure representing various Para-Analyser lattices, each of which is selected according to the quality of the evidence, that is updated with respect to measurements executed at run time. The resulting three-dimensional paraconsistent analysis structure is encapsulated as an expert-system cell called Cubic Paraconsistent Analyser with Evidence Filter and Temporal Analysis (CPAet). Sets of CPAet nodes are combined constituting distinct network topologies that are used to infer the operating conditions of equipment in data networks of electrical systems control and supervision, resulting in a fault detection and classification system. Tests on simulated systems show that the CPAet networks were effective to deal with inconsistencies without trivialising the inferences, while also providing a more informed (finer grained) decision on the data network equipment state, when compared to the traditional paraconsistent analysers.

This paper introduces a new formalism for the automated solution of spatial scenarios involving strings and holed objects. In particular, we revisit a previous formalisation that allows string loops to be treated as holes, but make a substantial modification by removing a previous limitation that prevented a string to cross its own loops. The formalisation introduced in the present paper relies on string segments as basic entities and achieves a greater degree of elaboration tolerance by using inertia to describe those parts of the physical scenario that are unaffected by a given action. As a representation language, we have used Temporal Answer Set Programming since it provides a simple and natural way to deal with time and inertia while, at the same time, it is accompanied by the automated tool 'telingo' that allows a systematic testing of the effects of any sequence of actions. As an illustrative example, we have studied the African Ring puzzle, a problem involving loops crossed by a unique string, and provided the first formalisation of its solution, to the best of our knowledge.

The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.

Abstract In this paper, we present a formalism for handling polysemy in spatial expressions based on supervaluation semantics called standpoint semantics for polysemy (SSP). The goal of this formalism is, given a prepositional phrase, to define its possible spatial interpretations. For this, we propose to characterize spatial prepositions by means of a triplet $\langle $image schema, semantic feature, spatial axis$\rangle $. The core of SSP is predicate grounding theories, which are formulas of a first-order language that define a spatial preposition through the semantic features of its trajector and landmark. Precisifications are also established, which are a set of formulae of a qualitative spatial reasoning formalism that aims to provide the spatial characterization of the trajector with respect to the landmark. In addition to the theoretical model, we also present results of a computational implementation of SSP for the preposition ‘in’.

Spatial knowledge plays an essential role in human reasoning, permitting tasks such as locating objects in the world (including oneself), reasoning about everyday actions and describing perceptual information. This is also the case in the field of mobile robotics, where one of the most basic (and essential) tasks is the autonomous determination of the pose of a robot with respect to a map, given its perception of the environment. This is the problem of robot self-localisation (or simply the localisation problem). This paper presents a probabilistic algorithm for robot self-localisation that is based on a topological map constructed from the observation of spatial occlusion. Distinct locations on the map are defined by means of a classical formalism for qualitative spatial reasoning, whose base definitions are closer to the human categorisation of space than traditional, numerical, localisation procedures. The approach herein proposed was systematically evaluated through experiments using a mobile robot equipped with a RGB-D sensor. The results obtained show that the localisation algorithm is successful in locating the robot in qualitatively distinct regions.

This paper presents initial results towards the development of a logic-based probabilistic event recognition system capable of learning and inferring high-level joint actions from simultaneous task execution demonstrations on a search and rescue scenario. We adopt a probabilistic extension of the Event Calculus defined over Markov Logic Networks (MLN-EC). This formalism was applied to learn and infer the actions of human operators teleoperating robots in a real-world robotic search and rescue task. Experimental results in both simulation and real robots show that the probabilistic event logic can recognise the actions taken by the human teleoperators in real world domains containing two collaborating robots, even with uncertain and noisy data.

This paper presents results of a mobile robot qualitative self-localisation experiment using information from cast shadows. We present results of self-localisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other the threshold is set according to a prediction about the robot's location, given a shadow-based map defined upon a qualitative spatial reasoning theory. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform egolocation and to calibrate a robot's interpretation of its perceptual input.

Recently, cognitive psychologists and others have turned their attention to the formerly neglected study of shadows, and the information they purvey. These studies show that the human perceptual system values information from shadows very highly, particularly in the perception of depth, even to the detriment of other cues. However with a few notable exceptions, computer vision systems have treated shadows not as signal but as noise. This paper makes a step towards redressing this imbalance by considering the formal representation of shadows. We take one particular aspect of reasoning about shadows, developing the idea that shadows carry information about a fragment of the viewpoint of the light source. We start from the observation that the region on which the shadow is cast is occluded by the caster with respect to the light source and build a qualitative theory about shadows using a region-based spatial formalism about occlusion. Using this spatial formalism and a machine vision system we are able to draw simple conclusions about domain objects and egolocation for a mobile robot.

Reasoning about perception of depth and about spatial relations between moving physical objects is a challenging problem. We investigate the representation of depth and motion by means of depth profiles whereby each object in the world is represented as a single peak. We propose a logical theory, formulated in the situation calculus (SC), that is used for reasoning about object motion (including motion of the observer). The theory proposed here is comprehensive enough to accommodate reasoning about both sensor data and actions in the world. We show that reasoning about depth profiles is sound and complete with respect to actual motion in the world. This shows that in the conceptual neighbourhood diagram (CND) of all possible depth perceptions, the transitions between perceptions are logical consequences of the proposed theory of depth and motion.