An integrated intelligent system includes a first intelligent electronic device, a second intelligent electronic device, a transferable intelligent control device (TICD) and a cross product bus The first intelligent electronic device performs a first function and the second intelligent electronic device performs a second function The cross product bus couples the first intelligent electronic device to the transferable intelligent control device The TICD partially controls behaviors of the intelligent electronic device by sending commands over the cross product bus to the first intelligent electronic device and the TICD partially controls behaviors of the second intelligent electronic device to perform the second function The TICD is first attached to the first intelligent electronic device to partially control the behaviors of the first electronic device, then detached from the first electronic device, and then attached to the second intelligent electronic device to perform the second function

Transferable intelligent control device

Universal Access in Human-Computer Interaction. Ambient Interaction

Robotic applications are typically realized using ad hoc and domain-specific solutions, which challenges the engineering and cross-project reuse of such applications. Especially in complex scenarios, where self-adaptive robots collaborate among themselves or with humans, the effective and systematic engineering of such applications is becoming increasingly important. Such scenarios require decentralized software architectures that foster fault-tolerant ways of managing large teams of (possibly) heterogeneous robots. To the best of our knowledge, no existing architecture for robot applications supports decentralized and self-adaptive collaboration. To address this gap, we conducted a design science study with 21 practitioners and experts in the field of robotics to develop an architecture fulfilling these requirements through several iterations. We present SERA, an architecture for robot applications that supports human-robot collaboration, as well as adaptation and coordination of single- and multi-robot systems in a decentralized fashion. SERA is based on layers that contain components that manage the adaptation at different levels of abstraction and communicate through well-defined interfaces. We successfully validated SERA by considering a set of real scenarios, by both using simulators and real robots, by involving robotic experts, and by benchmarking it with state-of-the-art solutions.

An Architecture for Decentralized, Collaborative, and Autonomous Robots

Episodic memory has been examined in different disciplines such as psychology and neuroscience for more than 30 years. Now, engineering and computer science are developing an increasing interest in episodic memory for artificial systems. In this paper, we propose a novel framework referred to as EPIROME to develop and investigate high-level episodic memory mechanisms which can be used to model and compare episodic memories of high-level events for technical systems. We applied the framework in the domain of service robotics to enable our service robot TASER to collect autobiographical memories to improve action planning based on past experiences. The framework provides the robot with a life-long memory since past experiences can be stored and reloaded. In practise, one main advantage of our episodic memory is that it provides one- shot learning capabilities to our robot. This reduces the demerit of other learning strategies where learning takes too long when used with a real robot system in natural environments and therefore is not feasible.

/pdf/epirome-a-novel-framework-to-investigate-high-level-episodic-3esphy2l5q.pdf

EPIROME - A novel framework to investigate high-level episodic robot memory

This paper proposes a method for multi-modal prediction of intention based on a probabilistic description of movement primitives and goals. We target dyadic interaction between a human and a robot in a collaborative scenario. The robot acquires multi-modal models of collaborative action primitives containing gaze cues from the human partner and kinetic information about the manipulation primitives of its arm. We show that if the partner guides the robot with the gaze cue, the robot recognizes the intended action primitive even in the case of ambiguous actions. Furthermore, this prior knowledge acquired by gaze greatly improves the prediction of the future intended trajectory during a physical interaction. Results with the humanoid iCub are presented and discussed.

/pdf/multi-modal-intention-prediction-with-probabilistic-movement-1cbegv7de3.pdf

Multi-Modal Intention Prediction With Probabilistic Movement Primitives

A sensor-based model of a service robot's environment is a prerequisite for interaction. Such a model should contain the positions of the robot's interaction partners. Additionally to the actual positions of the partners it is important for the service robot to predict their possible future positions. This knowledge could for example be used to realize efficient path planning for delivery tasks. In this paper we propose an extensible framework for systems that combine different sensor modalities in a general tracking system. Exemplarily, a tracking system is implemented that fuses tracking algorithms in laser range scans as well as in camera images by a particle filter. We point out the real-time capabilities of our tracking algorithms implementation. Practical experiments show that multimodality increases the system's robustness to incorrect measurements of single sensors. Furthermore, the observed trajectories are generalized to trajectory patterns by a novel method which uses Self Organizing Maps. Those patterns are used to predict trajectories of the currently observed persons. It is also demonstrated that a self organizing map is suitable for learning and generalizing trajectories. Convenient predictions of future trajectories are presented which are deduced from these generalizations.

Multimodal People Tracking and Trajectory Prediction based on Learned Generalized Motion Patterns

In this paper we propose a novel concept for the programming of multi-modal service robots. The presented software architecture eases the development of high-level applications for service robots. The software architecture is based upon the Roblet-Technology, which is a powerful medium for robots. It introduces the possibility to develop, compile and execute an application on one workstation. Since the Roblet-Technology uses Java the development is independent of the operation system. With the feature of running programs as a distributed software, the framework allows running algorithms which need great computation power on different machines which provide this power. In this way, it greatly improves programming and testing of applications in service robotics. The concept is evaluated in the context of the service robot TASER of the TAMS Institute at the University of Hamburg. This robot consists of a mobile platform with two manipulators equipped with artificial hands. Several multimodal input and output devices for interaction round off the robot.

A flexible software architecture for multi-modal service robots

A sensor-based model of a service robot?s environment is a prerequisite for interaction Such a model should contain the positions of the robot?s interaction partners Many reasonable applications require this knowledge in real-time It could for example be used to realize efficient path planning for delivery tasks Additionally to the actual positions of the partners it is important for the service robot to predict their possible future positions In this paper we propose an extensible framework that combines different sensor modalities in a general real-time tracking system Exemplarily, a tracking system is implemented that fuses tracking algorithms in laser range scans as well as in camera images by a particle filter Furthermore, human trajectories are predicted by deducing them from learned motion patterns The observed trajectories are generalized to trajectory patterns by a novel method which uses Self Organizing Maps Those patterns are used to predict trajectories of the currently observed persons Practical experiments show that multimodality increases the system?s robustness to incorrect measurements of single sensors It is also demonstrated that a Self Organizing Map is suitable for learning and generalizing trajectories Convenient predictions of future trajectories are presented which are deduced from these generalizations

Real-Time Fusion of Multimodal Tracking Data and Generalization of Motion Patterns for Trajectory Prediction

Visual programming by demonstration of grasping skills in the context of a mobile service robot using 1D-topology based self-organizing-maps

This paper presents a novel approach to natural demonstration of manipulation skills for multimodal interactive robots. The main focus is on the natural demonstration of manipulation skills, especially grasping skills. In order to teach grasping skills to a multimodal interactive robot, a human instructor makes use of natural spoken language and grasping actions demonstrated to the robot. The proposed approach emphasizes on four different aspects of learning by demonstration: First, the dialog system for processing natural speech is considered. Second, an object detection and classification scheme for the robot is shown. Third, the correspondence problem is addressed by an algorithm for visual tracking of the demonstrator's hands in real time and the transformation of the tracking results into an approach trajectory for a robotic arm. The fourth aspect addresses the fine-tuning of the robot's hand configuration for each grasp. It introduces a criterion to evaluate a grasp for stability and possible reuse of a grasped object. The approach produces stable grasps and is applied and evaluated on a multimodal service robot.

/pdf/natural-demonstration-of-manipulation-skills-for-multimodal-yb5wnr6t78.pdf

Natural demonstration of manipulation skills for multimodal interactive robots

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