This paper details the development and use of a scientific experimental apparatus providing participants with an immersive experience of flight by means of a head-mounted display showing them hovering in a virtual world. It aims to understand the technological and human mechanisms at work when users identify themselves with a self-avatar perceived from a third-person perspective. The design principles that made it possible to manufacture this device are explained for each aspect. Then, two sets of raw results obtained by questionnaires or interviews will be presented, concerning respectively 43 test subjects in a controlled setting and 91 festival goers, in order to show how the methodology and the gathered data have confirmed the capacity of our experience to highlight different adaptation strategies in the face of one's self-avatar and the effects of bilocation.

In this work we present the head assembly and the gaze shifting capabilities of the bipedal humanoid robot Romeo. The purpose of the head system is to provide a reliable hardware platform for Human-Robot Interaction (HRI) applications, stereo vision based navigation or gazing experiments, either as stand-alone version or in synergy with the body movements. Towards this purpose, the number of joints, the angular range, speed and acceleration of the eyes and neck rotations should be should be in agreement with human psychophysics, i.e. reproducing realistic kinematics. We present a mechatronic system of the head and neck; accompanied by experimental results of head-fixed eye movements, solely head rotations, as well as eye-head cooperative motion for large angles of gaze shifting. We compare the values achieved by the prototype, with those of an average adult human.

The emotional component of human walking patterns can be characterized by a limited set of kinematic cues [8]. Here, we tested whether artificial synthesis of emotional gaits based on these cues can facilitate emotion perception in human observers. To this purpose, we recorded neutral gaits and artificially modified the walking speed, the upper-body posture or some combination thereof. Nave observers had to judge emotion conveyed by these animated avatars or by real emotional gaits recorded in professional actors. We found that the recognition rates of both animated and real movies were comparable and particularly high (e.g. > 85 %): all emotions but fear were unambiguously perceived in walking avatars thanks to a particular combination of walking speed and head/trunk posture. This reveals a strong coupling between motor and perceptual processes underlying emotion expression and recognition. The potential applications of these findings in the fields of animated motion picture and humanoid robotics are discussed.

Passivity-based walkers represent a model for human walking and a solution for low-energy locomotion for humanoid robots. The presence of an upper-body and even a head in this kind of systems is necessary as a better model for humans and to improve their usability. The benefits of these additions have never been studied, and no work experimented the addition of a head limb to a passivity-based walker. So, we aim, in this paper, to study the effects of the addition of these modifications on walkers with fully passive lower limbs. By comparing three systems (a passive compass, an upper-body stabilizing walker and a head stabilizing walker) simulations show that: (ii) upper-body stabilization improves the stability of the walking limit cycle; (ii) in return, the stabilization of the upper body requires a noticeable amount of the kinetic energy of the walker, and a significant energy supply (steeper slopes for the passive case) is necessary to guarantee the stability of the gait, especially for the case of head stabilization; and (iii) in a dynamical context, such as steep slopes, the upper-body and head stabilization have close performances for absorbing perturbations and smoothing the impacts, but with a slight advantage for the latter.

Advanced driving simulators aim at rendering the motion of a vehicle with maximum fidelity, which requires increased mechanical travel, size, and cost of the system. Motion cueing algorithms reduce the motion envelope by taking advantage of limitations in human motion perception, and the most commonly employed method is just to scale down the physical motion. However, little is known on the effects of motion scaling on motion perception and on actual driving performance. This paper presents the results of a European collaborative project, which explored different motion scale factors in a slalom driving task. Three state-of-the-art simulator systems were used, which were capable of generating displacements of several meters. The results of four comparable driving experiments, which were obtained with a total of 65 participants, indicate a preference for motion scale factors below 1, within a wide range of acceptable values (0.4-0.75). Very reduced or absent motion cues significantly degrade driving performance. Applications of this research are discussed for the design of motion systems and cueing algorithms for driving simulation.

This paper describes a walking stabilization control on a soft ground based on gait analysis for a humanoid robot. There are many researches on gait analysis on a hard ground, but few scientists analyze the walking ability of human beings on a soft ground. Therefore, we conducted anthropometric measurement using a motion capture system on a soft ground. By analyzing experimental data, we obtained two findings. The first finding is that although there are no significant differences in step width and step length, step height tends to increase to avoid the collision between the feet and a soft ground. The second finding is that there are no significant differences in the lateral CoM trajectories but the vertical CoM amplitude increases when walking on a soft ground. Based on these findings, we developed a walking stabilization control to stabilize the CoM motion in the lateral direction on a soft ground. Verification of the proposed control is conducted through experiments with a human-sized humanoid robot WABIAN-2R. The experimental videos are supplemented.

The results of the neuroscientific research show that humans tend to stabilize the head orientation during locomotion. In this paper we describe the implementation of inverse kinematics based head stabilization controller on the humanoid platform. The controller uses the IMU feedback and controls neck joints in order to align the head orientation with the global orientation reference. Thanks to the method, we can decouple the orientational motion of the head from the rest of the body. This way stabilized head becomes better platform for proprioceptive sensory apparatus, such as cameras or IMU. In the paper we present three experiments which prove that the method has good performance in damping both, high and low frequency motion of the head. We also prove that the proposed controller improves the stability of the tracked goal point on the image of in-built camera.

In this work we propose an adaptive model for the head stabilization based on a feedback error learning (FEL). This model is capable to overcome the delays caused by the head motor system and adapts itself to the dynamics of the head motion. It has been designed to track an arbitrary reference orientation for the head in space and reject the disturbance caused by trunk motion. For efficient error learning we use the recursive least square algorithm (RLS), a Newton-like method which guarantees very fast convergence. Moreover, we implement a neural network to compute the rotational part of the head inverse kinematics. Verification of the proposed control is conducted through experiments with Matlab SIMULINK and a humanoid robot SABIAN.

Recent litterature suggested a top-down organization of locomotion, with the head being the stable frame of reference and the gaze anticipating the future direction. The present paper provides detailed investigation into the spatial and temporal relationship among the gaze and each body segment during locomotion on a complex trajectory without visual cues or guidance. We established that gaze direction anticipates on head which anticipates on other body segments, and showed that mean and variance of anticipations are very dependent on curvature changes on the trajectory. This idea of top down organization of locomotion may challenge the concept of control of locomotion in humanoid robotics, since it has so far largely depended on ZMP which is related to the force acting between the foot and the ground.

We present a novel method of guiding a humanoid robot, including stepping, by allowing a user to move its head. The motivation behind this approach comes from research in the field of human neuroscience. In human locomotion it has been found that the head plays a very important role in guiding and planning motion. We use this idea to generate humanoid whole-body motion derived purely as a result of moving the head joint. The input to move the head joint is provided by a user via a 6D mouse. The algorithm presented in this study judges when further head movement leads to instability, and then generates stepping motions to stabilize the robot. By providing the software with autonomy to decide when and where to step, the user is allowed to simply steer the robot head (via visual feedback) without worrying about stability. We illustrate our results by presenting experiments conducted in simulation, as well as on our robot, HRP2.

Human movements show several prominent features; movement duration is nearly independent of movement size (the isochrony principle), instantaneous speed depends on movement curvature (captured by the 2/3 power law), and complex movements are composed of simpler elements (movement compositionality). No existing theory can successfully account for all of these features, and the nature of the underlying motion primitives is still unknown. Also unknown is how the brain selects movement duration. Here we present a new theory of movement timing based on geometrical invariance. We propose that movement duration and compositionality arise from cooperation among Euclidian, equi-affine and full affine geometries. Each geometry posses a canonical measure of distance along curves, an invariant arc-length parameter. We suggest that for continuous movements, the actual movement duration reflects a particular tensorial mixture of these canonical parameters. Near geometrical singularities, specific combinations are selected to compensate for time expansion or compression in individual parameters. The theory was mathematically formulated using Cartan's moving frame method. Its predictions were tested on three data sets: drawings of elliptical curves, locomotion and drawing trajectories of complex figural forms (cloverleaves, lemniscates and limaçons, with varying ratios between the sizes of the large versus the small loops). Our theory accounted well for the kinematic and temporal features of these movements, in most cases better than the constrained Minimum Jerk model, even when taking into account the number of estimated free parameters. During both drawing and locomotion equi-affine geometry was the most dominant geometry, with affine geometry second most important during drawing; Euclidian geometry was second most important during locomotion. We further discuss the implications of this theory: the origin of the dominance of equi-affine geometry, the possibility that the brain uses different mixtures of these geometries to encode movement duration and speed, and the ontogeny of such representations.

This talk will deal with the neural basis of spatial memory during navigation. When navigating or trying to remember a traveled path the brain uses different cognitive strategies. It can use, among others, an egocentric, (topo-kinesthetic), memory of the travel involving kinaesthetic memories of the route and episodic memory, but it can also use allocentric, (topo-graphic), map like, memories. Different brain systems are involved in these strategies and they develop during ontogeny. I will describe studies using virtual reality in normal subjects and patient with hippocampal lesions. In addition I will describe results obtained with fMRI and intracranial recordings in epileptic patients which identify the brain areas involved in these strategies. Virtual reality allows us to selectively identify the brain areas involved in such tasks as perspective change, manipulation of reference frames, decision making and some aspects of social interaction such as empathy etc. The paradigms we have designed for these fundamental studies can also be used for diagnosis of these deficits in Schizophrenia, autism, and other psyhchiatric or neurological diseases. They could also be used for remediation in these diseases or others like agoraphobia. A new field is now opened in which neuroscientists, neurologists, psychiatrists, otolaryngologists and roboticians can cooperate to try to compensate for this category of deficits in patients, during development or aging.

In this paper, we investigate different possible strategies underlying the formation of human locomotor trajectories in goal-directed walking. Seven subjects were asked to walk within a motion capture facility from a fixed starting point and direction, and to cross over distant porches for which both position and direction in the room were changed over trials. Stereotyped trajectories were observed in the different subjects. The underlying idea to attack this question has been to relate this problem to an optimal control scheme: the trajectory is chosen according to some optimization principle. This is our basic starting assumption. The subject being viewed as a controlled system, we tried to identify several criteria that could be optimized. Is it the time to perform the trajectory? The length of the path? The minimum jerk along the path? We found that the variation (time derivative) of the curvature of the locomotor paths is minimized. Moreover, we show that the human locomotor trajectories are well approximated by the geodesics of a differential system minimizing the norm of the control. Such geodesics are made of arcs of clothoids. The clothoid or Cornu spiral is a curve, whose curvature grows with the distance from the origin.