In two experiments, subjects traveled through virtual mazes, encountering target objects along the way. Their task was to indicate the direction to these target objects from a terminal location in the maze (from which the objects could no longer be seen). Subjects controlled their motion through the mazes using three locomotion modes. In the Walk mode, subjects walked normally in the experimental room. For each subject, body position and heading were tracked, and the tracking information was used to continuously update the visual imagery presented to the subjects on a head-mounted display. This process created the impression of immersion in the experimental maze. In the Visual Turn mode subjects moved through the environment using a joystick to control their turning. The only sensory information subjects received about rotation and translation was that provided by the computer-generated imagery. The Real Turn mode was midway between the other two modes, in that subjects physically turned in place to steer while translating in the virtual maze; thus translation through the maze was signaled only by the computer-generated imagery,

Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to precisely define the existing concepts and terminologies, so as to comprehensively describe the different theories and models within the same unifying framework. We present navigation strategies within a 4 level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computat...

Our experiences with a range of evolutionary robotic experiments have resulted in major changes to our set-up of artificial life experiments and our interpretation of observed phenomena. Initially, we investigated simulation-reality relationships in order to transfer our artificial life simulation work with evolution of neural network agents to real robots. This is a difficult task, but can, in a lot of cases, be solved with a carefully built simulator. By being able to evolve control mechanisms for physical robots, we were able to study biological hypotheses about animal behaviours by using exactly the same experimental set-ups as were used in the animal behavioural experiments. Evolutionary robotic experiments with rats open field box experiments and chick detours show how evolutionary robotics can be a powerful biological tool, and they also suggest that incremental learning might be fruitful for achieving complex robot behaviour in an evolutionary context. However, it is not enough...

This paper describes how an animat endowed with the MonaLysa control architecture can build a cognitive map that merges into a hierarchical framework not only topological links between landmarks, but also higher-level structures, control information, and metric distances and orientations. The paper also describes how the animat can use such a map to locate itself, even if it is endowed with noisy dead-reckoning capacities. MonaLysa's mapping and self-positioning capacities are illustrated by results obtained in three different environments and four noise-level conditions. These capacities appear to be gracefully degraded when the environment grows more challenging and when the noise level increases. In the discussion, the current approach is compared to others with similar objectives, and directions for future work are outlined. Keywords Hierarchical map. Topological information. Metric information. Landmarks. Self-positioning. Dead-reckoning. Robustness to noise. 1 Introduction In...

This paper describes how the MonaLysa control architecture implements a route-following navigation strategy. Two procedures that allow map building and self-positioning are described, and experimental results are provided that demonstrate that such procedures are robust with respect to noise. This approach is compared to others with similar objectives, and directions for future work are outlined. 1 Introduction In robotics or animat research, traditional navigation methods that use internal geometrical representations of the environment (Latombe, 1991) are confronted with various implementation difficulties, due to memory and time requirements, as well as sensory and motor errors (Nehmzow, 1995). To overcome these difficulties, several researchers (Chatila and Laumond, 1985; Kuipers and Byun, 1991; Mataric, 1992; Nehmzow, 1995) have advocated the use of various types of topological models to represent the connectivity of the environment, and several such models have been devised that...

. Internal representations of the environment are often invoked to explain performance in tasks in which an organism must make a detour around an obstacle to reach a target and the organism can lose sight of the target along the path to the target. By simulating a detour task in evolving populations of robots (Khepera) we show that neural networks with memory units perform better than networks without memory units in this task. However, the content of the memory units need not be interpreted as an internal representation of the position of target. The memory units send a time-varying internally generated input to the network's hidden units that allows the network to generate the appropriate behavior even when there is no external input. Networks without memory units do not have this internal input and this explains their inferior performance. 1 Detour behavior Imagine a robot (an artificial organism) that must reach a target object located somewhere in the environment. The robot has a...

In two experiments, subjects traveled through virtual mazes, encountering target objects along the way. Their task was to indicate the direction to these target objects from a terminal location in the maze (from which the objects could no longer be seen). Subjects controlled their motion through the mazes using three locomotion modes. In the Walk mode, subjects walked normally in the experimental room. For each subject, body position and heading were tracked, and the tracking information was used to continuously update the visual imagery presented to the subjects on a head-mounted display. This process created the impression of immersion in the experimental maze. In the Visual Turn mode subjects moved through the environment using a joystick to control their turning. The only sensory information subjects received about rotation and translation was that provided by the computer-generated imagery. The Real Turn mode was midway between the other two modes, in that subjects physically turned in place to steer while translating in the virtual maze; thus translation through the maze was signaled only by the computer-generated imagery, whereas rotations were signaled by the imagery as well as by proprioceptive and vestibular information. The dependent measure in the experiment was the absolute error of the subject's directional estimate to each target from the terminal location. Performance in the Walk mode was significantly better than in the Visual Turn mode but other trends were not significant. A secondary finding was that the degree of motion sickness depended upon locomotion mode, with the lowest incidence occurring in the Walk mode. Both findings suggest the advisability of having subjects explore virtual environments using real rotations and translations in tasks invol...