Interactive semantic mapping: Experimental evaluation

Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping

From teleoperation to the cognitive human-robot interface

Robots are slowly moving from factories to mines, construction sites, public places and homes. This new type of robot or robotized working machine – field and service robots (FSR) – should be capable of performing different kinds of tasks in unstructured changing environments, not only among humans but through continuous interaction with humans. The main requirements for an FSR are mobility, advanced perception capabilities, high "intelligence" and easy interaction with humans. Although mobility and perception capabilities are no longer bottlenecks, they can nevertheless still be greatly improved. The main bottlenecks are intelligence and the human - robot interface (HRI). Despite huge efforts in "artificial intelligence" research, the robots and computers are still very "stupid" and there are no major advancements on the horizon. This emphasizes the importance of the HRI. In the subtasks, where high-level cognition or intelligence is needed, the robot has to ask for help from the operator. In addition to task commands and supervision, the HRI has to provide the possibility of exchanging information about the task and environment through continuous dialogue and even methods for direct teleoperation. The thesis describes the development from teleoperation to service robot interfaces and analyses the usability aspects of both teleoperation/telepresence systems and robot interfaces based on high-level cognitive interaction. The analogue in the development of teleoperation interfaces and HRIs is also pointed out. The teleoperation and telepresence interfaces are studied on the basis of a set of experiments in which the different enhancement-level telepresence systems were tested in different tasks of a driving type. The study is concluded by comparing the usability aspects and the feeling of presence in a telepresence system. HRIs are studied with an experimental service robot WorkPartner. Different kinds of direct teleoperation, dialogue and spatial information interfaces are presented and tested. The concepts of cognitive interface and common presence are presented. Finally, the usability aspects of a human service robot interface are discussed and evaluated.reviewe

Grounding robot motion in natural language and visual perception

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks. I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct this research on a custom-built six-wheeled mobile robot. First I present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports learning the meanings of nouns and prepositions from sentential descriptions of paths driven by the robot, as well as using such meanings to both generate a sentential description of a path and perform automated driving of a path specified in natural language. One limitation of this framework is that it requires as input the locations of the (initially nameless) objects in the floor plan. Next I present a method to automatically detect, localize, and label objects in the robot’s environment using only the robot’s video feed and corresponding odometry. This method produces a map of the robot’s environment in which objects are differentiated by abstract class labels. Finally, I present work that unifies the previous two approaches. This method detects, localizes, and labels objects, as the previous method does. However, this new method integrates natural-language descriptions to learn actual object names, rather than abstract labels

RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics

Intelligence for Human-Assistant Planetary Surface Robots

The central premise in developing effective human-assistant planetary surface robots is that robotic intelligence is needed. The exact type, method, forms and/or quantity of intelligence is an open issue being explored on the ERA project, as well as others. In addition to field testing, theoretical research into this area can help provide answers on how to design future planetary robots. Many fundamental intelligence issues are discussed by Murphy [2], including (a) learning, (b) planning, (c) reasoning, (d) problem solving, (e) knowledge representation, and (f) computer vision (stereo tracking, gestures). The new "social interaction/emotional" form of intelligence that some consider critical to Human Robot Interaction (HRI) can also be addressed by human assistant planetary surface robots, as human operators feel more comfortable working with a robot when the robot is verbally (or even physically) interacting with them. Arkin [3] and Murphy are both proponents of the hybrid deliberative-reasoning/reactive-execution architecture as the best general architecture for fully realizing robot potential, and the robots discussed herein implement a design continuously progressing toward this hybrid philosophy. The remainder of this chapter will describe the challenges associated with robotic assistance to astronauts, our general research approach, the intelligence incorporated into our robots, and the results and lessons learned from over six years of testing human-assistant mobile robots in field settings relevant to planetary exploration. The chapter concludes with some key considerations for future work in this area