Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Scene understanding by robotic interactive perception

This thesis presents a novel and generic visual architecture for scene understanding by robotic interactive perception. This proposed visual architecture is fully integrated into autonomous systems performing object perception and manipulation tasks. The proposed visual architecture uses interaction with the scene, in order to improve scene understanding substantially over non-interactive models. Specifically, this thesis presents two experimental validations of an autonomous system interacting with the scene: Firstly, an autonomous gaze control model is investigated, where the vision sensor directs its gaze to satisfy a scene exploration task. Secondly, autonomous interactive perception is investigated, where objects in the scene are repositioned by robotic manipulation. The proposed visual architecture for scene understanding involving perception and manipulation tasks has four components: 1) A reliable vision system, 2) Camera-hand eye calibration to integrate the vision system into an autonomous robot’s kinematic frame chain, 3) A visual model performing perception tasks and providing required knowledge for interaction with scene, and finally, 4) A manipulation model which, using knowledge received from the perception model, chooses an appropriate action (from a set of simple actions) to satisfy a manipulation task. This thesis presents contributions for each of the aforementioned components. Firstly, a portable active binocular robot vision architecture that integrates a number of visual behaviours are presented. This active vision architecture has the ability to verge, localise, recognise and simultaneously identify multiple target object instances. The portability and functional accuracy of the proposed vision architecture is demonstrated by carrying out both qualitative and comparative analyses using different robot hardware configurations, feature extraction techniques and scene perspectives. Secondly, a camera and hand-eye calibration methodology for integrating an active binocular robot head within a dual-arm robot are described. For this purpose, the forward kinematic model of the active robot head is derived and the methodology for calibrating and integrating the robot head is described in detail. A rigid calibration methodology has been implemented to provide a closed-form hand-to-eye calibration chain and this has been extended with a mechanism to allow the camera external parameters to be updated dynamically for optimal 3D reconstruction to meet the requirements for robotic tasks such as grasping and manipulating rigid and deformable objects. It is shown from experimental results that the robot head achieves an overall accuracy of fewer than 0.3 millimetres while recovering the 3D structure of a scene. In addition, a comparative study between current RGB-D cameras and our active stereo head within two dual-arm robotic test-beds is reported that demonstrates the accuracy and portability of our proposed methodology. Thirdly, this thesis proposes a visual perception model for the task of category-wise objects sorting, based on Gaussian Process (GP) classification that is capable of recognising objects categories from point cloud data. In this approach, Fast Point Feature Histogram (FPFH) features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide a probability estimate of the identity of the object and serves the key role of modelling perception confidence in the interactive perception cycle. The interaction stage is responsible for invoking the appropriate action skills as required to confirm the identity of an observed object with high confidence as a result of executing multiple perception-action cycles. The recognition accuracy of the proposed perception model has been validated based on simulation input data using both Support Vector Machine (SVM) and GP based multi-class classifiers. Results obtained during this investigation demonstrate that by using a GP-based classifier, it is possible to obtain true positive classification rates of up to 80\%. Experimental validation of the above semi-autonomous object sorting system shows that the proposed GP based interactive sorting approach outperforms random sorting by up to 30\% when applied to scenes comprising configurations of household objects. Finally, a fully autonomous visual architecture is presented that has been developed to accommodate manipulation skills for an autonomous system to interact with the scene by object manipulation. This proposed visual architecture is mainly made of two stages: 1) A perception stage, that is a modified version of the aforementioned visual interaction model, 2) An interaction stage, that performs a set of ad-hoc actions relying on the information received from the perception stage. More specifically, the interaction stage simply reasons over the information (class label and associated probabilistic confidence score) received from perception stage to choose one of the following two actions: 1) An object class has been identified with high confidence, so remove from the scene and place it in the designated basket/bin for that particular class. 2) An object class has been identified with less probabilistic confidence, since from observation and inspired from the human behaviour of inspecting doubtful objects, an action is chosen to further investigate that object in order to confirm the object’s identity by capturing more images from different views in isolation. The perception stage then processes these views, hence multiple perception-action/interaction cycles take place. From an application perspective, the task of autonomous category based objects sorting is performed and the experimental design for the task is described in detail

Development of a Semi-Autonomous Robotic System to Assist Children with Autism in Developing Visual Perspective Taking Skills

Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.Peer reviewe

Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions

A Portable Active Binocular Robot Vision Architecture for Scene Exploration

We present a portable active binocular robot vision archi- tecture that integrates a number of visual behaviours. This vision archi- tecture inherits the abilities of vergence, localisation, recognition and si- multaneous identification of multiple target object instances. To demon- strate the portability of our vision architecture, we carry out qualitative and comparative analysis under two different hardware robotic settings, feature extraction techniques and viewpoints. Our portable active binoc- ular robot vision architecture achieved average recognition rates of 93.5% for fronto-parallel viewpoints and, 83% percentage for anthropomorphic viewpoints, respectively