Abstract—Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. We use a probabilistic graphical model that factors into three key components. The first component grounds landmark phrases such as “the computers ” in the perceptual frame of the robot by exploiting co-occurrence statistics from a database of tagged images such as Flickr. Second, a spatial reasoning component judges how well spatial relations such as “past the computers ” describe a path. Finally, verb phrases such as “turn right ” are modeled according to the amount of change in orientation in the path. Our system follows 60 % of the directions in our corpus to within 15 meters of the true destination, significantly outperforming other approaches. I.

Abstract — Autonomous mobile robots are deployed in a variety of application domains, resulting in scenario specific implementations. However these systems share common components responsible for perception, path planning and task execution. In order to find a formal way to identify the influence of the environmental complexity to the used methods, an approach for quantitative system interdependence analysis is introduced. The coherence between several performance indicators of different system components, as well as the influence of environmental parameters on the system, are learned and quantitatively evaluated. Performance evaluation of an autonomous robot navigating in two different urban environments is conducted and presented results demonstrate the applicability of the proposed approach. I.

Abstract — In this paper an autonomous switching between two basic attention selection mechanisms, top-down and bottom-up, is proposed, substituting manual switching. This approach fills the gab in object search using conventional topdown biased bottom-up attention selection: the latter one fails, if a group of objects is searched whose appearances can not be uniquely described by low-level features used in bottomup computation models. Two internal robot states, observing and operating, are included to determine the visual selection behavior. A vision guided mobile robot, equipped with an active stereo camera, is used to demonstrate our strategy and evaluate the performance experimentally. I.

Abstract — For path planning algorithms of robots it is important that the robot does not reach a state of inevitable collision. In crowded environments with many humans or robots, the set of possible inevitable collision states (ICS) is often unacceptably high, such that the robot has to stop and wait in too many situations. For this reason, the concept of ICS is extended to probabilistic collision states (PCS), which estimates the collision probability for a given state. This allows to efficiently run planning algorithms through crowded environments when accepting a certain collision probability. A further novelty is that the obstacles possibly react to the robot in order to mitigate the risk of a collision. The results show a significant difference in interaction behavior. Thus, this approach is especially suited for active and non-deterministic moving obstacles in the robot workspace. I.

Abstract. Robots that are to assist humans in flexible and versatile ways, will not always possess all the information required to fulfill their task. Therefore robotic systems have to be able to retrieve information through natural language communication with humans. Natural language is often vague or ambiguous and thus hard to interpret by technical systems. To enable robotic systems to interpret natural language expressions correctly it is necessary to include findings from human-human communication into the dialog systems of robots. In this work the field of communication topics between human and robot is confined to the communication about space, more specifically to a robot asking a human for directions. This paper gives insight on theories from linguistics research focussing on asking for and giving directions. From these theories 10 heuristic rules for human-robot interaction are deduced, where 4 of the rules apply even to systems that are not able to communicate through natural language. Additionally a first experiment where a robot used the 4 basic heuristic rules to successfully ask passers-by for directions and find the way to an unknown goal location is presented. 1

A biologically inspired foveated attention system in an object detection scenario is proposed. Bottom-up attention is applied on a wide-angle stereo camera to select a sequence of fixation points. Successive snapshots of high foveal resolution using a telephoto camera enable highly accurate object recognition based on SIFT algorithm. Top-down information is incrementally estimated and integrated using a Kalman filter, enabling parameter adaptation to changing environments due to robot locomotion. In the experimental evaluation, all the target objects were detected in different backgrounds. Evident improvements in accuracy, flexibility and efficiency are achieved.

Abstract—This paper reports about a human-robot interaction field trial conducted with the autonomous mobile robot ACE (Autonomous City Explorer) in a public place, where the ACE robot needs the support of human passers-by to find its way to a target location. Since the robot does not possess any prior map knowledge or GPS support, it has to acquire missing information through interaction with humans. The robot thus has to initiate communication by asking for the way, and retrieves information from passers-by showing the way by gestures (pointing) and marking goal positions on a still image on the touch screen of the robot. The aims of the field trial where threefold: (1) Investigating the aptitude of the navigation architecture, (2) Evaluating the intuitiveness of the interaction concept for the passers-by, (3) Assessing people’s willingness to support the ACE robot in its task, i.e. assessing the social acceptability. The field trial demonstrates that the architecture enables successful autonomous path finding without any prior map knowledge just by route directions given by passers-by. An additional street survey and observational data moreover attests the intuitiveness of the interaction paradigm and the high acceptability of the ACE robot in the public place. Keywords-autonomous mobile robot; human-robot interaction; field trial; social acceptance; I.