Generation of dynamic motion for anthropomorphic systems under prioritized equality and inequality constraints

In this paper, we propose a solution to compute full-dynamic motions for a humanoid robot, accounting for various kinds of constraints such as dynamic balance or joint limits. As a first step, we propose a unification of task-based control schemes, in inverse kinematics or inverse dynamics. Based on this unification, we generalize the cascade of quadratic programs that were developed for inverse kinematics only. Then, we apply the solution to generate, in simulation, wholebody motions for a humanoid robot in unilateral contact with the ground, while ensuring the dynamic balance on a non horizontal surface

A robotics approach for interpreting the gaze-related modulation of the activity of premotor neurons during reaching

International audienceThis paper deals with the modeling of the activity of premotor neurons associated with the execution of a visually guided reaching movement in primates. We address this question from a robotics point of view, by considering a simplified kinematic model of the head, eye and arm joints. By using the formalism of visual servoing, we show that the hand controller depends on the direction of the head and the eye, as soon as the hand-target difference vector is expressed in eye-centered coordinates. Based on this result, we propose a new interpretation of previous electrophysiological recordings in monkey, showing the existence of a gaze-related modulation of the activity of premotor neurons during reaching. This approach sheds a new light on this phenomenon which, so far, is not clearly understood

Sur la nature subjective et incarnée de l’intelligence humaine et son rapport à l’omniprésence de l’intelligence numérique

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On using human movement invariants to generate target-driven anthropomorphic locomotion

We present a method for generating anthropomorphic motion by studying `invariants\u27 in human movements and applying them as kinematic tasks. We recorded whole-body motion of 14 participants during a walking and grasping task and performed a detailed analysis in order to synthesize the stereotypy in human motion as rules. We propose an algorithm that produces the key parameters of motion taking into account the knowledge from human movement, and the limitations of the anthropomorph. We generalize our results such that we can create motion parameters for objects which were not in the original protocol. The algorithmic output is applied in a task based prioritized inverse kinematics solver to generate dynamically stable and realistic anthropomorphic motion. We illustrate our results on the humanoid HRP-2 by making it walk to and grasp objects at various positions

A Weak Generalized Inverse Applied to Redundancy Solving of Serial Chain Robots

International audienceA peculiar form of right inverse derived from the theory of rectangular matrix determinants is considered instead of the classic Moore-Penrose psueodinverse with the aim to get compact symbolic expressions for the redundancy solving of serial chain robots. Such an approach, essentially based on the of the closed-form expressions of the m m minors of the n m robot Jacobian (m<n), is proposed as a new way for fast computation in inverse kin-ematic control

Steering a humanoid robot by its head

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

On using methods from robotics to study human task dependent balance during whole-body pointing and drawing movements

The human body is capable of a complex variety of movements and the redundant nature of the anthropomorphic structure makes it an interesting system to study from a robotics point of view. In this paper we study the strategies used by humans to balance during whole body pointing and drawing movements as a function of several simultaneously executed tasks. We record the motion of participants using motion capture, force-plates and electromyography. Joint kinematics, motion of center of pressure and center of gravity and joint torques are then estimated using a complex musculoskeletal model. The effects of executing secondary tasks (balancing on one foot, or holding a cup in place) were studied by analyzing the accuracy of movement, the center of mass and center of pressure displacement, the coordination of the lower and upper body, and the modulation of muscle activity. Our results show that the nominal motion rules can be modified by the addition of a secondary objective. The approach used in this study is an interesting extension to conventional human movement studies and provides an alternative way to understand human movements using methodologies from robotics

Prototyping filter-sum beamformers for sound source localization in mobile robotics

International audienceThe work presented in this paper comes as a part of a project which aims at developing an auditory system for a mobile robot. It presents a sound source localization strategy which enables the sensing of signals within a direction of arrival and frequency domain of interest while rejecting other data. A rapid prototyping method is proposed to design filter-sum beamformers on the basis of convex optimization. This method is well-suited to robotics applications as it copes with real-time constraints and allows the localization of broadband signals such as human voice. Numerous simulation results are used to illustrate the reasoning

Optimal Estimation of the Centroidal Dynamics of Legged Robots

International audienceEstimating the centroidal dynamics of legged robots is crucial in the context of multi-contact locomotion of legged robots. In this paper, we formulate the estimation of centroidal dynamics as a maximum a posteriori problem and we use a differential dynamic programming approach for solving it. The soundness of the proposed approach is first validated on a simulated humanoid robot, where ground truth data is available, enabling error analysis, and then compared to other alternatives of the state of the art, namely an extend Kalman filter and a recursive complementary filter. The results demonstrate that, compared to other approaches, the proposed method reduces the estimation error on the centroidal state in addition to ensuring the dynamics consistency of the state trajectory. Finally, the effectiveness of the proposed method is illustrated on real measurements, obtained from walking experiments with the HRP-2 humanoid robot

Computational details for : "Optimal Estimation of the Centroidal Dynamics of Legged Robots"

This document complements the paper entitled "Differential Dynamic Programming for Maximum a Posteriori Centroidal State Estimation of Legged Robots" [1]. The purpose of this work was to estimate the centroidal dynamics of legged robots by formulating a maximum a posteriori problem and solving it thanks to differential dynamic programming (DDP)