Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.Comment: 7 pages, 6 figures. Accepted to ICRA202

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits

Categorization of Affordances and Prediction of Future Object Interactions using Qualitative Spatial Relations

The application of deep neural networks on robotic platforms has successfully advanced robot perception in tasks related to human-robot collaboration scenarios. Tasks such as scene understanding, object categorization, affordance detection, interaction anticipation, are facilitated by the acquisition of knowledge about the object interactions taking place in the scene. The contributions of this thesis are two-fold: 1) it shows how representations of object interactions learned in an unsupervised way can be used to predict categories of objects depending on the affordances; 2) it shows how future frame-independent interaction can be learned in a self-supervised way by exploiting high-level graph representations of the object interactions. The aim of this research is to create representations and perform predictions of interactions which abstract from the image space and attain generalization across various scenes and objects. Interactions can be static, eg. holding a bottle, as well as dynamic, eg. playing with a ball, where the temporal aspect of the sequence of several static interactions is of importance to make the dynamic interaction distinguishable. Moreover, occlusion of objects in the 2D domain should be handled to avoid false positive interaction detections. Thus, RGB-D video data is exploited for these tasks. As humans tend to use objects in many different ways depending on the scene and the objects' availability, learning object affordances in everyday-life scenarios is a challenging task, particularly in the presence of an open-set of interactions and class-agnostic objects. In order to abstract from the continuous representation of spatio-temporal interactions in video data, a novel set of high-level qualitative depth-informed spatial relations is presented. Learning similarities via an unsupervised method exploiting graph representations of object interactions induces a hierarchy of clusters of objects with similar affordances. The proposed method handles object occlusions by capturing effectively possible interactions and without imposing any object or scene constraints. Moreover, interaction and action anticipation remains a challenging problem, especially considering the generalizability constraints of trained models from visual data or exploiting visual video embeddings. State of the art methods allow predictions approximately up to three seconds of time in the future. Hence, most everyday-life activities, which consist of actions of more than five seconds in duration, are not predictable. This thesis presents a novel approach for solving the task of interaction anticipation between objects in a video scene by utilizing high-level qualitative frame-number-independent spatial graphs to represent object interactions. A deep recurrent neural network learns in a self-supervised way to predict graph structures of future object interactions, whilst being decoupled from the visual information, the underlying activity, and the duration of each interaction taking place. Finally, the proposed methods are evaluated on RGB-D video datasets capturing everyday-life activities of human agents, and are compared against closely-related and state-of-the-art methods

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Capacitive Sensing and Communication for Ubiquitous Interaction and Environmental Perception

During the last decade, the functionalities of electronic devices within a living environment constantly increased. Besides the personal computer, now tablet PCs, smart household appliances, and smartwatches enriched the technology landscape. The trend towards an ever-growing number of computing systems has resulted in many highly heterogeneous human-machine interfaces. Users are forced to adapt to technology instead of having the technology adapt to them. Gathering context information about the user is a key factor for improving the interaction experience. Emerging wearable devices show the benefits of sophisticated sensors which make interaction more efficient, natural, and enjoyable. However, many technologies still lack of these desirable properties, motivating me to work towards new ways of sensing a user's actions and thus enriching the context. In my dissertation I follow a human-centric approach which ranges from sensing hand movements to recognizing whole-body interactions with objects. This goal can be approached with a vast variety of novel and existing sensing approaches. I focused on perceiving the environment with quasi-electrostatic fields by making use of capacitive coupling between devices and objects. Following this approach, it is possible to implement interfaces that are able to recognize gestures, body movements and manipulations of the environment at typical distances up to 50cm. These sensors usually have a limited resolution and can be sensitive to other conductive objects or electrical devices that affect electric fields. The technique allows for designing very energy-efficient and high-speed sensors that can be deployed unobtrusively underneath any kind of non-conductive surface. Compared to other sensing techniques, exploiting capacitive coupling also has a low impact on a user's perceived privacy. In this work, I also aim at enhancing the interaction experience with new perceptional capabilities based on capacitive coupling. I follow a bottom-up methodology and begin by presenting two low-level approaches for environmental perception. In order to perceive a user in detail, I present a rapid prototyping toolkit for capacitive proximity sensing. The prototyping toolkit shows significant advancements in terms of temporal and spatial resolution. Due to some limitations, namely the inability to determine the identity and fine-grained manipulations of objects, I contribute a generic method for communications based on capacitive coupling. The method allows for designing highly interactive systems that can exchange information through air and the human body. I furthermore show how human body parts can be recognized from capacitive proximity sensors. The method is able to extract multiple object parameters and track body parts in real-time. I conclude my thesis with contributions in the domain of context-aware devices and explicit gesture-recognition systems

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text