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Human navigation in built environments depends on symbolic spatial information which has unrealized potential to enhance robot navigation capabilities. Information sources, such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open-source implementation to encourage future work in the area of symbolic navigation. The symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. This article concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.

RoboCup is an international research initiative that encourages research in the fields of robotics and AI, with a particular focus on developing cooperation between autonomous agents in dynamic multiagent environments. […], The Fourth Robotic Soccer World Championships (RoboCup-2000) was held from 27 August to 3 September 2000 at the Melbourne Exhibition Center in Melbourne, Australia. In total, 83 teams, consisting of about 500 people, participated in RoboCup-2000, and about 5000 spectators watched the events. RoboCup-2000 showed dramatic improvement over past years in each of the existing robotic soccer leagues (legged, small size, mid size, and simulation) and introduced RoboCup Jr. competitions and RoboCup Rescue and Humanoid demonstration events. The RoboCup Workshop, held in conjunction with the championships, provided a forum for the exchange of ideas and experiences among the different leagues. This article summarizes the advances seen at RoboCup-2000, including reports from the championship teams and overviews of all the RoboCup events.

Robots are able to learn how to interact with objects by developing computational models of affordance. This paper presents an approach in which learning and operation occur concurrently, toward achieving lifelong affordance learning. In a such a regime a robot must be able to learn about new objects, but without a general rule for what an “object” is, the robot must learn about everything in the environment to determine their affordances. In this paper, sensorimotor coordination is modeled using a distributed semi-Markov decision process; it is created online during robot operation, and performs continual action selection to reach a goal state. In an initial experiment we show that this model captures an object's affordances, which are exploited to perform several different tasks using a mobile robot equipped with a gripper and infrared “tactile” sensor. In a secondary experiment, we show that the robot can learn that the marker is the only visual feature that can be gripped and that walls and floor do not have the affordance of being “grip-able.” The distributed mechanism is necessary for the modeling of multiple sensory stimuli simultaneously, and the selection of the object with the necessary affordances for the task is emergent from the robot's actions, while other parts of the environment that are perceived, such as walls and floors, are ignored.

This paper presents the design, implementation and evaluation of a trainable vision guided mobile robot. The robot, CORGI, has a CCD camera as its only sensor which it is trained to use for a variety of tasks. The techniques used for training and the choice of natural light vision as the primary sensor makes the methodology immediately applicable to tasks such as trash collection or fruit picking. For example, the robot is readily trained to perform a ball finding task which involves avoiding obstacles and aligning with tennis balls. The robot is able to move at speeds up to 0.8 ms-1while performing this task, and has never had a collision in the trained environment. It can process video and update the actuators at 11 Hz using a single $20 microprocessor to perform all computation. Further results are shown to evaluate the system for generalization across unseen domains, fault tolerance and dynamic environments.