Who am I talking with? A face memory for social robots

In order to provide personalized services and to develop human-like interaction capabilities robots need to rec- ognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide user- aware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot’s system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets

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

A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

Smart Camera Robotic Assistant for Laparoscopic Surgery

The cognitive architecture also includes learning mechanisms to adapt the behavior of the robot to the different ways of working of surgeons, and to improve the robot behavior through experience, in a similar way as a human assistant would do. The theoretical concepts of this dissertation have been validated both through in-vitro experimentation in the labs of medical robotics of the University of Malaga and through in-vivo experimentation with pigs in the IACE Center (Instituto Andaluz de Cirugía Experimental), performed by expert surgeons.In the last decades, laparoscopic surgery has become a daily practice in operating rooms worldwide, which evolution is tending towards less invasive techniques. In this scenario, robotics has found a wide field of application, from slave robotic systems that replicate the movements of the surgeon to autonomous robots able to assist the surgeon in certain maneuvers or to perform autonomous surgical tasks. However, these systems require the direct supervision of the surgeon, and its capacity of making decisions and adapting to dynamic environments is very limited. This PhD dissertation presents the design and implementation of a smart camera robotic assistant to collaborate with the surgeon in a real surgical environment. First, it presents the design of a novel camera robotic assistant able to augment the capacities of current vision systems. This robotic assistant is based on an intra-abdominal camera robot, which is completely inserted into the patient’s abdomen and it can be freely moved along the abdominal cavity by means of magnetic interaction with an external magnet. To provide the camera with the autonomy of motion, the external magnet is coupled to the end effector of a robotic arm, which controls the shift of the camera robot along the abdominal wall. This way, the robotic assistant proposed in this dissertation has six degrees of freedom, which allow providing a wider field of view compared to the traditional vision systems, and also to have different perspectives of the operating area. On the other hand, the intelligence of the system is based on a cognitive architecture specially designed for autonomous collaboration with the surgeon in real surgical environments. The proposed architecture simulates the behavior of a human assistant, with a natural and intuitive human-robot interface for the communication between the robot and the surgeon

Knowledge Representation for Robots through Human-Robot Interaction

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP 201