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.
Interfaces based on Monitor, Mouse, and Keyboard are a simple and cheap alternative that has been applied to a wide variety of robotics applications like robot navigation @cite_26 , grasping @cite_66 @cite_56 @cite_113 , or object manipulation @cite_16 @cite_53 .
@cite_26 @cite_53 @cite_113 @cite_56 @cite_16 @cite_66
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Robust remote teleoperation of high-DOF manipulators is of critical importance across a wide range of robotics applications. Contemporary robot manipulation interfaces primarily utilize a free-positioning pose specification approach to independently control each axis of translation and orientation in free space. In this work, we present two novel interfaces, constrained positioning and point-and-click, which incorporate scene information, including points-of-interest and local surface geometry, into the grasp specification process. We also present results of a user study evaluation comparing the effects of increased use of scene information in grasp pose specification algorithms for general object manipulation. The results of our study show that constrained positioning and point-and-click significantly outperform the widely used free positioning approach by significantly reducing the number of grasping errors and the number of user interactions required to specify poses. Furthermore, the point-and-click interface significantly increased the number of tasks users were able to complete. Remote control robots are being found in an increasing number of application domains, including search and rescue, exploration, and reconnaissance. There is a large body of HRI research that investigates interface design for remote navigation, control, and sensor monitoring, while aiming for interface enhancements that benefit the remote operator such as improving ease of use, reducing operator mental load, and maximizing awareness of a robot's state and remote environment. Even though many remote control robots have multi-degree-of-freedom robotic manipulator arms for interacting with the environment, there is only limited research into easy-to-use remote control interfaces for such manipulators, and many commercial robotic products are still using simplistic interface technologies such as keypads or gamepads with arbitrary mappings to arm morphology. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. 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. We present a mobile manipulation platform operated by a motor-impaired person using input from a head-tracker, single-button mouse. The platform is used to perform varied and unscripted manipulation tasks in a real home, combining navigation, perception and manipulation. The operator can make use of a wide range of interaction methods and tools, from direct tele-operation of the gripper or mobile base to autonomous sub-modules performing collision-free base navigation or arm motion planning. We describe the complete set of tools that enable the execution of complex tasks, and share the lessons learned from testing them in a real user's home. In the context of grasping, we show how the use of autonomous sub-modules improves performance in complex, cluttered environments, and compare the results to those obtained by novice, able-bodied users operating the same system.