La robotique humanoïde arrive a maturité avec des robots plus rapides et plus précis. Pour faire face à la complexité mécanique, la recherche a commencé à regarder au-delà du cadre habituel de la robotique, vers les sciences de la vie, afin de mieux organiser le contrôle du mouvement. Cette thèse explore le lien entre mouvement humain et le contrôle des systèmes anthropomorphes tels que les robots humanoïdes. Tout d’abord, en utilisant des méthodes classiques de la robotique, telles que l’optimisation, nous étudions les principes qui sont à la base de mouvements répétitifs humains, tels que ceux effectués lorsqu’on joue au yoyo. Nous nous concentrons ensuite sur la locomotion en nous inspirant de résultats en neurosciences qui mettent en évidence le rôle de la tête dans la marche humaine. En développant une interface permettant à un utilisateur de commander la tête du robot, nous proposons une méthode de contrôle du mouvement corps-complet d’un robot humanoïde, incluant la production de pas et permettant au corps de suivre le mouvement de la tête. Cette idée est poursuivie dans l’étude finale dans laquelle nous analysons la locomotion de sujets humains, dirigée vers une cible, afin d’extraire des caractéristiques du mouvement sous forme invariants. En faisant le lien entre la notion “d’invariant” en neurosciences et celle de “tâche cinématique” en robotique humanoïde, nous développons une méthode pour produire une locomotion réaliste pour d’autres systèmes anthropomorphes. Dans ce cas, les résultats sont illustrés sur le robot humanoïde HRP2 du LAAS-CNRS. La contribution générale de cette thèse est de montrer que, bien que la planification de mouvement pour les robots humanoïdes peut être traitée par des méthodes classiques de robotique, la production de mouvements réalistes nécessite de combiner ces méthodes à l’observation systématique et formelle du comportement humain. / Humanoid robotics is coming of age with faster and more agile robots. To compliment the physical complexity of humanoid robots, the robotics algorithms being developed to derive their motion have also become progressively complex. The work in this thesis spans across two research fields, human neuroscience and humanoid robotics, and brings some ideas from the former to aid the latter. By exploring the anthropological link between the structure of a human and that of a humanoid robot we aim to guide conventional robotics methods like local optimization and task-based inverse kinematics towards more realistic human-like solutions. First, we look at dynamic manipulation of human hand trajectories while playing with a yoyo. By recording human yoyo playing, we identify the control scheme used as well as a detailed dynamic model of the hand-yoyo system. Using optimization this model is then used to implement stable yoyo-playing within the kinematic and dynamic limits of the humanoid HRP-2. The thesis then extends its focus to human and humanoid locomotion. We take inspiration from human neuroscience research on the role of the head in human walking and implement a humanoid robotics analogy to this. By allowing a user to steer the head of a humanoid, we develop a control method to generate deliberative whole-body humanoid motion including stepping, purely as a consequence of the head movement. This idea of understanding locomotion as a consequence of reaching a goal is extended in the final study where we look at human motion in more detail. Here, we aim to draw to a link between “invariants” in neuroscience and “kinematic tasks” in humanoid robotics. We record and extract stereotypical characteristics of human movements during a walking and grasping task. These results are then normalized and generalized such that they can be regenerated for other anthropomorphic figures with different kinematic limits than that of humans. The final experiments show a generalized stack of tasks that can generate realistic walking and grasping motion for the humanoid HRP-2. The general contribution of this thesis is in showing that while motion planning for humanoid robots can be tackled by classical methods of robotics, the production of realistic movements necessitate the combination of these methods with the systematic and formal observation of human behavior.

Speed-accuracy tradeoffs, velocity profiles, and physiological tremor are fundamental characteristics of human movement. The principles underlying these phenomena have long attracted major interest and controversy. Each is well established experimentally but as yet they have no common theoretical basis. It is proposed that these three phenomena occur as the direct consequence of a movement response planning system that acts as an intermittent optimal controller operating at discrete intervals of ~100 ms. The BUMP model of response planning describes such a system. It forms the kernel of adaptive model theory which defines, in computational terms, a basic unit of motor production or BUMP. Each BUMP consists of three processes: (i) analysing sensory information, (ii) planning a desired optimal response, and (iii) executing that response. These processes operate in parallel across successive sequential BUMPs. The response planning process requires a discrete time interval in which to generate a minimum acceleration trajectory of variable duration, or horizon, to connect the actual response with the predicted future state of the target and compensate for executional error. BUMP model simulation studies show that intermittent adaptive optimal control employing two extremes of variable horizon predictive control reproduces almost exactly findings from several authoritative human experiments. On the one extreme, simulating spatially-constrained movements, a receding horizon strategy results in a logarithmic speed-accuracy tradeoff and accompanying asymmetrical velocity profiles. On the other extreme, simulating temporally-constrained movements, a fixed horizon strategy results in a linear speed-accuracy tradeoff and accompanying symmetrical velocity profiles. Furthermore, simulating ramp movements, a receding horizon strategy closely reproduces experimental observations of 10 Hz physiological tremor. A 100 ms planning interval yields waveforms and power spectra equivalent to those of joint-angle, angular velocity and electromyogram signals recorded for several speeds, directions, and skill levels of finger movement. While other models of response planning account for one or other set of experimentally observed features of speed-accuracy tradeoffs, velocity profiles, and physiological tremor, none accounts for all three. The BUMP model succeeds in explaining these disparate movement phenomena within a single framework, strengthening this approach as the foundation for a unified theory of motor control and planning.

Speed-accuracy tradeoffs, velocity profiles, and physiological tremor are fundamental characteristics of human movement. The principles underlying these phenomena have long attracted major interest and controversy. Each is well established experimentally but as yet they have no common theoretical basis. It is proposed that these three phenomena occur as the direct consequence of a movement response planning system that acts as an intermittent optimal controller operating at discrete intervals of ~100 ms. The BUMP model of response planning describes such a system. It forms the kernel of adaptive model theory which defines, in computational terms, a basic unit of motor production or BUMP. Each BUMP consists of three processes: (i) analysing sensory information, (ii) planning a desired optimal response, and (iii) executing that response. These processes operate in parallel across successive sequential BUMPs. The response planning process requires a discrete time interval in which to generate a minimum acceleration trajectory of variable duration, or horizon, to connect the actual response with the predicted future state of the target and compensate for executional error. BUMP model simulation studies show that intermittent adaptive optimal control employing two extremes of variable horizon predictive control reproduces almost exactly findings from several authoritative human experiments. On the one extreme, simulating spatially-constrained movements, a receding horizon strategy results in a logarithmic speed-accuracy tradeoff and accompanying asymmetrical velocity profiles. On the other extreme, simulating temporally-constrained movements, a fixed horizon strategy results in a linear speed-accuracy tradeoff and accompanying symmetrical velocity profiles. Furthermore, simulating ramp movements, a receding horizon strategy closely reproduces experimental observations of 10 Hz physiological tremor. A 100 ms planning interval yields waveforms and power spectra equivalent to those of joint-angle, angular velocity and electromyogram signals recorded for several speeds, directions, and skill levels of finger movement. While other models of response planning account for one or other set of experimentally observed features of speed-accuracy tradeoffs, velocity profiles, and physiological tremor, none accounts for all three. The BUMP model succeeds in explaining these disparate movement phenomena within a single framework, strengthening this approach as the foundation for a unified theory of motor control and planning.