006 Spezielle Computerverfahren
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Low-Cost In-Hand Slippage Detection and Avoidance for Robust Robotic Grasping with Compliant Fingers
(2021)
Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
This paper introduces FaceHaptics, a novel haptic display based on a robot arm attached to a head-mounted virtual reality display. It provides localized, multi-directional and movable haptic cues in the form of wind, warmth, moving and single-point touch events and water spray to dedicated parts of the face not covered by the head-mounted display.The easily extensible system, however, can principally mount any type of compact haptic actuator or object. User study 1 showed that users appreciate the directional resolution of cues, and can judge wind direction well, especially when they move their head and wind direction is adjusted dynamically to compensate for head rotations. Study 2 showed that adding FaceHaptics cues to a VR walkthrough can significantly improve user experience, presence, and emotional responses.
Towards self-explaining social robots. Verbal explanation strategies for a needs-based architecture
(2019)
In order to establish long-term relationships with users, social companion robots and their behaviors need to be comprehensible. Purely reactive behavior such as answering questions or following commands can be readily interpreted by users. However, the robot's proactive behaviors, included in order to increase liveliness and improve the user experience, often raise a need for explanation. In this paper, we provide a concept to produce accessible “why-explanations” for the goal-directed behavior an autonomous, lively robot might produce. To this end we present an architecture that provides reasons for behaviors in terms of comprehensible needs and strategies of the robot, and we propose a model for generating different kinds of explanations.
This paper describes a dynamic, model-based approach for estimating intensities of 22 out of 44 different basic facial muscle movements. These movements are defined as Action Units (AU) in the Facial Action Coding System (FACS) [1]. The maximum facial shape deformations that can be caused by the 22 AUs are represented as vectors in an anatomically based, deformable, point-based face model. The amount of deformation along these vectors represent the AU intensities, and its valid range is [0, 1]. An Extended Kalman Filter (EKF) with state constraints is used to estimate the AU intensities. The focus of this paper is on the modeling of constraints in order to impose the anatomically valid AU intensity range of [0, 1]. Two process models are considered, namely constant velocity and driven mass-spring-damper. The results show the temporal smoothing and disambiguation effect of the constrained EKF approach, when compared to the frame-by-frame model fitting approach ‘Regularized Landmark Mean-Shift (RLMS)’ [2]. This effect led to more than 35% increase in performance on a database of posed facial expressions.
Towards explaining deep learning networks to distinguish facial expressions of pain and emotions
(2018)
Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.
Kleinere, günstigere und effizientere Sensoren und Aktoren sowie Funkprotokolle haben dazu geführt, dass Smart Home Produkte in zunehmend auch für den privaten Massenmarkt erschwinglich werden. Damit stehen Hersteller und Anbieter vor der Herausforderung, komplexe cyber-physische Systeme für Jedermann handhabbar zu gestalten. Es fehlen allerdings empirische Erkenntnisse über die Rolle von Smart Home im Alltag. Wir präsentieren Ergebnisse aus einer Living Lab Studie, in der 14 Haushalte mit einer am Markt erhältlichen Smart Home Nachrüstlösung ausgestattet und über neun Monate empirisch begleitet wurden. Anhand der Analyse von Interviews, Beobachtungen und Co-Design Workshops in den Phasen der Produktauswahl, Installation, Konfiguration und längerfristigen Nutzung zeigen wir Herausforderungen und Potentiale von Smart Home Systemen auf. Unsere Erkenntnisse deuten darauf hin, dass das Smart Home immer noch von technischen Details dominiert wird. Zugleich fehlen Nutzern angemessene Steuerungs- und Kontrollmöglichkeiten, um weiterhin die Entscheidungshoheit im eigenen Zuhause zu behalten.