We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
One important aspect of GUI design is to consider the user and the objectives. Several studies have looked at what humans can and can't do in these robotics tasks, what interaction exists between an individual's abilities, and what the interface can do @cite_38 @cite_106 . In addition, studies involving tasks in 3D space usually consider human natural abilities or previous experience in similar environments @cite_16 . Indeed, studies suggest that spatial reasoning ability not only varies between individuals, but that different tasks might require different abilities within the realm of spatial reasoning @cite_81 @cite_41 . One common precept in HCD is to avoid visual clutter in order to lower cognitive load. Studies have found that novice users get better results on interfaces that hide extraneous features @cite_46 , and where they only focus on the high-level tasks while the robot controls the low level details @cite_15 .
@cite_38 @cite_15 @cite_41 @cite_81 @cite_106 @cite_46 @cite_16
2020456813 1970387420 2160641905 2088147743
In daily human interactions, spatial reasoning occupies an important place. In this paper we present a situation assessment reasoner that generates relevant symbolic information from the geometry of the environment with respect to relations between objects and human capabilities. The role of SPARK (SPAtial Reasoning and Knowledge) component is to permanently maintain a state of the world in order to provide a basis for the robot to plan, to act, to react and to interact. More precisely, we describe here the way the system manages the hypotheses to be able to handle such knowledge in a flexible manner. Equipped with such capabilities, a robot that will interact with humans should be able to extract, compute or infer these relations and capabilities in order to communicate and interact efficiently in a natural way. To illustrate our work, we will explain how the robot is able to manage and update agents beliefs and pass Sally-Anne test. This work is part of a broader effort to develop a complete decisional framework for human-robot interactive task achievement. The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot. Given the ease that humans have with using a keyboard and mouse in typical, non-colocated computer interaction, many studies have investigated the value of colocating the visual field and motor workspaces using immersive display modalities. Significant understanding has been gained by previous work comparing physical tasks against virtual tasks, visuomotor colocation versus non-colocation, and even visuomotor rotational misalignments in virtual environments (VEs). However, few studies have explored all of these paradigms in context with each other, and it is difficult to perform interstudy comparisons because of the variation in tested motor tasks. Therefore, using a stereoscopic fish tank display setup, the goal for the current study was to characterize human performance of a 3D Fitts' point-to-point reaching task using a stylus-based haptic interface in the physical, colocated non-colocated, and rotated VE visualization conditions. Five performance measures---throughput, efficiency, initial movement error, corrective movements, and peak velocity---were measured and used to evaluate task performance. These measures were studied in 22 subjects (11 male, 11 female, ages 20--32) performing a 3D variant of Fitts' serial task under 10 task conditions: physical, colocated VE, non-colocated VE, and rotated VEs from 45---315° in 45° increments. Hypotheses: All performance measures in the colocated VE were expected to reflect significantly reduced task performance over the real condition, but also reflect increased performance over the non-colocated VE condition. For rotational misalignments, all performance measures were expected to reflect the highest performance at 0°, reduce to the lowest performance at 90°, and rise again to a local maximum at 180° (symmetric about 0°). Results: All performance measures showed that the colocated VE condition resulted in significantly lower task performance than the physical condition and higher mean performance than the non-colocated VE condition, but the difference was not statistically significant. Also, rotation misalignments showed that task performance was mostly reduced to a minimum at 90°, 135°, and 225°. We conclude that colocated VEs may not significantly improve point-to-point reaching performance over non-colocated VEs. Also, visual rotations of ±45° affected throughput, efficiency, peak velocity, and initial movement error, but the number of corrective movements were not affected until ± 90°. This paper studies the impact of interfaces, allowing nonexpert users to efficiently and intuitively teach a robot to recognize new visual objects. We present challenges that need to be addressed for real-world deployment of robots capable of learning new visual objects in interaction with everyday users. We argue that in addition to robust machine learning and computer vision methods, well-designed interfaces are crucial for learning efficiency. In particular, we argue that interfaces can be key in helping nonexpert users to collect good learning examples and, thus, improve the performance of the overall learning system. Then, we present four alternative human–robot interfaces: Three are based on the use of a mediating artifact (smartphone, wiimote, wiimote and laser), and one is based on natural human gestures (with a Wizard-of-Oz recognition system). These interfaces mainly vary in the kind of feedback provided to the user, allowing him to understand more or less easily what the robot is perceiving and, thus, guide his way of providing training examples differently. We then evaluate the impact of these interfaces, in terms of learning efficiency, usability, and user’s experience, through a real world and large-scale user study. In this experiment, we asked participants to teach a robot 12 different new visual objects in the context of a robotic game. This game happens in a home-like environment and was designed to motivate and engage users in an interaction where using the system was meaningful. We then discuss results that show significant differences among interfaces. In particular, we show that interfaces such as the smartphone interface allows nonexpert users to intuitively provide much better training examples to the robot, which is almost as good as expert users who are trained for this task and are aware of the different visual perception and machine learning issues. We also show that artifact-mediated teaching is significantly more efficient for robot learning, and equally good in terms of usability and user’s experience, than teaching thanks to a gesture-based human-like interaction.