The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming

Towards Tutoring an Interactive Robot

Wrede B, Rohlfing K, Spexard TP, Fritsch J. Towards tutoring an interactive robot. In: Hackel M, ed. Humanoid Robots, Human-like Machines. ARS; 2007: 601-612.Many classical approaches developed so far for learning in a human-robot interaction setting have focussed on rather low level motor learning by imitation. Some doubts, however, have been casted on whether with this approach higher level functioning will be achieved. Higher level processes include, for example, the cognitive capability to assign meaning to actions in order to learn from the tutor. Such capabilities involve that an agent not only needs to be able to mimic the motoric movement of the action performed by the tutor. Rather, it understands the constraints, the means and the goal(s) of an action in the course of its learning process. Further support for this hypothesis comes from parent-infant instructions where it has been observed that parents are very sensitive and adaptive tutors who modify their behavior to the cognitive needs of their infant. Based on these insights, we have started our research agenda on analyzing and modeling learning in a communicative situation by analyzing parent-infant instruction scenarios with automatic methods. Results confirm the well known observation that parents modify their behavior when interacting with their infant. We assume that these modifications do not only serve to keep the infant’s attention but do indeed help the infant to understand the actual goal of an action including relevant information such as constraints and means by enabling it to structure the action into smaller, meaningful chunks. We were able to determine first objective measurements from video as well as audio streams that can serve as cues for this information in order to facilitate learning of actions

Evolution of Grasping Behaviour in Anthropomorphic Robotic Arms with Embodied Neural Controllers

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks

Tangible interaction with anthropomorphic smart objects in instrumented environments

A major technological trend is to augment everyday objects with sensing, computing and actuation power in order to provide new services beyond the objects' traditional purpose, indicating that such smart objects might become an integral part of our daily lives. To be able to interact with smart object systems, users will obviously need appropriate interfaces that regard their distinctive characteristics. Concepts of tangible and anthropomorphic user interfaces are combined in this dissertation to create a novel paradigm for smart object interaction. This work provides an exploration of the design space, introduces design guidelines, and provides a prototyping framework to support the realisation of the proposed interface paradigm. Furthermore, novel methods for expressing personality and emotion by auditory means are introduced and elaborated, constituting essential building blocks for anthropomorphised smart objects. Two experimental user studies are presented, confirming the endeavours to reflect personality attributes through prosody-modelled synthetic speech and to express emotional states through synthesised affect bursts. The dissertation concludes with three example applications, demonstrating the potentials of the concepts and methodologies elaborated in this thesis.Die Integration von Informationstechnologie in Gebrauchsgegenstände ist ein gegenwärtiger technologischer Trend, welcher es Alltagsgegenständen ermöglicht, durch den Einsatz von Sensorik, Aktorik und drahtloser Kommunikation neue Dienste anzubieten, die über den ursprünglichen Zweck des Objekts hinausgehen. Die Nutzung dieser sogenannten Smart Objects erfordert neuartige Benutzerschnittstellen, welche die speziellen Eigenschaften und Anwendungsbereiche solcher Systeme berücksichtigen. Konzepte aus den Bereichen Tangible Interaction und Anthropomorphe Benutzerschnittstellen werden in dieser Dissertation vereint, um ein neues Interaktionsparadigma für Smart Objects zu entwickeln. Die vorliegende Arbeit untersucht dafür die Gestaltungsmöglichkeiten und zeigt relevante Aspekte aus verwandten Disziplinen auf. Darauf aufbauend werden Richtlinien eingeführt, welche den Entwurf von Benutzerschnittstellen nach dem hier vorgestellten Ansatz begleiten und unterstützen sollen. Für eine prototypische Implementierung solcher Benutzerschnittstellen wird eine Architektur vorgestellt, welche die Anforderungen von Smart Object Systemen in instrumentierten Umgebungen berücksichtigt. Ein wichtiger Bestandteil stellt dabei die Sensorverarbeitung dar, welche unter anderem eine Interaktionserkennung am Objekt und damit auch eine physikalische Eingabe ermöglicht. Des Weiteren werden neuartige Methoden für den auditiven Ausdruck von Emotion und Persönlichkeit entwickelt, welche essentielle Bausteine für anthropomorphisierte Smart Objects darstellen und in Benutzerstudien untersucht wurden. Die Dissertation schliesst mit der Beschreibung von drei Applikationen, welche im Rahmen der Arbeit entwickelt wurden und das Potential der hier erarbeiteten Konzepte und Methoden widerspiegeln

Touch Technology in Affective Human, Robot, Virtual-Human Interactions: A Survey

Given the importance of affective touch in human interactions, technology designers are increasingly attempting to bring this modality to the core of interactive technology. Advances in haptics and touch-sensing technology have been critical to fostering interest in this area. In this survey, we review how affective touch is investigated to enhance and support the human experience with or through technology. We explore this question across three different research areas to highlight their epistemology, main findings, and the challenges that persist. First, we review affective touch technology through the human–computer interaction literature to understand how it has been applied to the mediation of human–human interaction and its roles in other human interactions particularly with oneself, augmented objects/media, and affect-aware devices. We further highlight the datasets and methods that have been investigated for automatic detection and interpretation of affective touch in this area. In addition, we discuss the modalities of affective touch expressions in both humans and technology in these interactions. Second, we separately review how affective touch has been explored in human–robot and real-human–virtual-human interactions where the technical challenges encountered and the types of experience aimed at are different. We conclude with a discussion of the gaps and challenges that emerge from the review to steer research in directions that are critical for advancing affective touch technology and recognition systems. In our discussion, we also raise ethical issues that should be considered for responsible innovation in this growing area

Embodied Decisions and the Predictive Brain

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective