In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.

Advances in autonomous driving and the introduction of automated valet parking allow the optimization of parking space. A future concept is high density valet parking with the potential to either reduce the extensive land use for parking or increase the capacity of existing parking facilities. This paper presents a novel approach that integrates high density parking into an existing parking lot, by explicitly making use of parking in the driving lane and reducing the shunting operations per vehicle. The proposed parking scheme allows vehicles to park either perpendicular to the driveway or in double-ended queues with k parking spots (k-deque) on the side of the driving lane. Leveraging the potential of such a layout increases the capacity of a parking lot by up to 25 %, while keeping the maximum number of shunts per car below [k/2] +1 between entry and exit. A dynamic simulation verifies the theoretical analysis and compares the performance of different deque lengths with respect to the distances traveled and the number of shunts per vehicle.

This paper presents a reasoning system for a multi-modal service robot with human-robot interaction. The reasoning system uses partially observable Markov decision processes (POMDPs) for decision making and an intermediate level for bridging the gap of abstraction between multi-modal real world sensors and actuators on the one hand and POMDP reasoning on the other. A filter system handles the abstraction of multi-modal perception while preserving uncertainty and model-soundness. A command sequencer is utilized to control the execution of symbolic POMDP decisions on multiple actuator components. By using POMDP reasoning, the robot is able to deal with uncertainty in both observation and prediction of human behavior and can balance risk and opportunity. The system has been implemented on a multi-modal service robot and is able to let the robot act autonomously in modeled human-robot interaction scenarios. Experiments evaluate the characteristics of the proposed algorithms and architecture.

Robots with the capability of learning new tasks from humans need the ability to transform gathered abstract task knowledge into their own representation and dimensionality. New task knowledge that has been acquired e.g. with Programming by Demonstration approaches by observing a human does not a-priori contain any robot-specific knowledge and actions, and is defined in the workspace and action space of the human demonstrator. This paper presents an approach for mapping abstract human-centered task knowledge to a robot execution system based on the target system properties. Therefore the required background knowledge about the target system is examined and defined explicitely. The mapping process is described based on this knowledge, and experiments and an evaluation are given.

Human activity recognition is an essential ability for service robots and other robotic systems which are in interaction with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if realtime constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a thorough analysis of features and classifiers aimed at human activity recognition is presented. Based on a set of 10 activities, the use of different feature selection algorithms is evaluated, as well as the results different classifiers (SVMs, Neural Networks, Bayesian Classifiers) provide in this context. Also the interdependency between feature selection method and chosen classifier is investigated. Furthermore, the optimal number of features to be used for an activity is examined.

Since many years the robotics community is envisioning robot assistants sharing the same environment with humans. It became obvious that they have to interact with humans and should adapt to individual user needs. Especially the high variety of tasks robot assistants will be facing requires a highly adaptive and user-friendly programming interface. One possible solution to this programming problem is the learning-by-demonstration paradigm, where the robot is supposed to observe the execution of a task, acquire task knowledge, and reproduce it. In this paper, a system to record, interpret, and reason over demonstrations of household tasks is presented. The focus is on the model-based representation of manipulation tasks, which serves as a basis for incremental reasoning over the acquired task knowledge. The aim of the reasoning is to condense and interconnect the data, resulting in more general task knowledge. A measure for the assessment of information content of task features is introduced. This measure for the relevance of certain features relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. Beside the autonomous information estimation of features, speech comments during the execution, pointing out the relevance of features are considered as well. The results of the incremental growth of the task knowledge when more task demonstrations become available and their fusion with relevance information gained from speech comments is demonstrated within the task of laying a table.

This paper proposes a tracking system called VooDoo for 3D tracking of human body movements based on a 3D body model and the iterative closest point (ICP) algorithm. The proposed approach is able to incorporate raw data from different input sensors, as well as results from feature trackers in 2D or 3D. All input data is processed within the same model fitting step by modeling all input measurements in 3D model space. The system has been implemented and runs in realtime at appr. 10-14 Hz. Experiments with complex human movements exhibit the characteristics and advantages of the proposed approach

In this paper we present a framework for grasp planning with a humanoid robot arm and a five-fingered hand. The aim is to provide the humanoid robot with the ability of grasping objects that appear in a kitchen environment. Our approach is based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace. This database is completed with two modules that make use of this object representation: an exhaustive offline grasp analysis system and a real-time stereo vision system. The offline grasp analysis system determines the best grasp for the objects by employing a simulation system, together with CAD models of the objects and the five-fingered hand. The results of this analysis are added to the object database using a description suited to the requirements of the grasp execution modules. A stereo camera system is used for a real-time object localization using a combination of appearance-based and model-based methods. The different components are integrated in a controller architecture to achieve manipulation task goals for the humanoid robot

Robot assistants in the same environment with humans have to interact with humans and learn or at least adapt to individual human needs. One of the core abilities is learning from human demonstrations, were the robot is supposed to observe the execution of a task, acquire task knowledge and reproduce it. In this paper, a system to interpret and reason over demonstrations of household tasks is presented. The focus is on the model based representation of manipulation tasks, which serves as a basis for reasoning over the acquired task knowledge. The aim of the reasoning is to condense and interconnect the knowledge. A measure for the assessment of information content of task features is introduced that relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. Beside the autonomous information estimation of features, speech comments during the execution, pointing out the relevance of features are considered as well

This paper presents an approach for human activity recognition focusing on gestures in a teaching scenario, together with the setup and results of user studies on human gestures exhibited in unconstrained human-robot interaction (HRI). The user studies analyze several aspects: the distribution of gestures, relations, and characteristics of these gestures, and the acceptability of different gesture types in a human-robot teaching scenario. The results are then evaluated with regard to the activity recognition approach. The main effort is to bridge the gap between human activity recognition methods on the one hand and naturally occuring or at least acceptable gestures for HRI on the other. The goal is two-fold: to provide recognition methods with information and requirements on the characteristics and features of human activities in HRI, and to identify human preferences and requirements for the recognition of gestures in human-robot teaching scenarios

This paper deals with building up a knowledge base of manipulation tasks by extracting relevant knowledge from demonstrations of manipulation problems. Hereby the focus of the paper is on modeling and representing manipulation tasks enabling the system to reason and reorganize the gathered knowledge in terms of reusability, scalability and explainability of learned skills and tasks. The goal is to compare the newly acquired skill or task with already existing task knowledge and decide whether to add a new task representation or to expand the existing representation with an alternative. Furthermore, a constraint for the representation is that at execution time the built knowledge base can be integrated and used in a symbolic planner.

This paper presents the distributed robot control software architecture developed for the autonomous service robot Albert2. The development of this architecture is focused on two major issues: modularity and the integration of learning aspects. Each module within the architecture is presented, as well as the underlying event-based communication framework. An approach for integration of learning capabilities is proposed.

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.