Torque Controlled Locomotion of a Biped Robot with Link Flexibility

When a big and heavy robot moves, it exerts large forces on the environment and on its own structure, its angular momentum can varysubstantially, and even the robot's structure can deform if there is a mechanical weakness. Under these conditions, standard locomotion controllers can fail easily. In this article, we propose a complete control scheme to work with heavy robots in torque control. The full centroidal dynamics is used to generate walking gaits online, link deflections are taken into account to estimate the robot posture and all postural instructions are designed to avoid conflicting with each other, improving balance. These choices reduce model and control errors, allowing our centroidal stabilizer to compensate for the remaining residual errors. The stabilizer and motion generator are designed together to ensure feasibility under the assumption of bounded errors. We deploy this scheme to control the locomotion of the humanoid robot Talos, whose hip links flex when walking. It allows us to reach steps of 35~cm, for an average speed of 25~cm/sec, which is among the best performances so far for torque-controlled electric robots.Comment: IEEE-RAS International Conference on Humanoid Robots (Humanoids 2022), IEEE, Nov 2022, Ginowan, Okinawa, Japa

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 msecs. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos

Generation of motion in mobile and humanoid robotics

La génération de mouvements de locomotion en robotique mobile est étudiée dans le monde académique depuis plusieurs décennies. La théorie concernant la modélisation et le contrôle des robots à roues est largement mature. Cependant, la mise en œuvre effective de ces modèles dans des conditions réelles demande des études complémentaires. Dans cette thèse, nous présentons trois projets mettant en œuvre trois différents types de robots mobiles. Nous débutons dans chaque cas par une analyse sur les qualités recherchées d’un mouvement dans un contexte particulier, qu’il soit artistique ou industriel, et terminons par la présentation des architectures algorithmiques et logicielles mises en œuvre, notamment dans le cadre d’expositions de plusieurs mois, où le public est invité à partager l’espace d’évolution de robots. La réalisation de ces projets montre que certains choix technologiques semblant insignifiants au moment de la conception des robots sont déterminants dans les dernières étapes de la production. On peut extrapoler cette remarque depuis ces robots mobile à deux ou trois degrés de liberté vers des robots humanoïdes pouvant en avoir plusieurs dizaines. La stratégie classique qui consiste à concevoir, dans un premier temps, l’architecture mécatronique des robots humanoïdes, pour se poser ensuite la question de leur contrôle, atteint ses limites, comme le montrent par exemple la consommation énergétique et la difficulté d’obtenir des mouvements de marche dynamique sur ces robots, pourtant conçus dans le but de marcher. Dans une perspective globale de conception des robots marcheurs, nous proposons un système de codesign, où il est possible d’optimiser simultanément la conception mécanique et les contrôleurs d’un robot..Generation of locomotion motions in mobile robotics has been studied in the academic world for several decades. The theory concerning the modeling and control of wheeled robots is largely mature. However, the actual implementation of these models in real conditions requires further studies. In this thesis, we present three projects using three different types of mobile robots. In each case, we begin with an analysis of the required qualities of a motion in a particular context, whether artistic or industrial, and end with the presentation of the algorithmic and software architectures implemented, particularly in the context of exhibitions of several months, where the public is invited to share the space of evolution of robots. The realization of these projects shows that some technological choices seem insignificant at the time of the design of the robots are decisive in the final stages of production. One can extrapolate this remark from these mobile robots with two or three degrees of freedom towards humanoid robots which can have several tens. The classical strategy of first designing the mechatronic architecture of humanoid robots and then raising the question of their control has reached its limits, as illustrated, for example, by their energy consumption and the difficulty to obtain dynamic walking motions on these robots, yet designed for the purpose of walking. From a global perspective of robot design, we propose a system of codesign, where it is possible to simultaneously optimize the mechanical design and the controllers of a robot. This system is firstly tested by various examples as proof of concept. It is then applied to the comparison of rigid and elastic actuators on different biped robots, then to the study of the impact of the stabilization of the head on the general stabilization of the body and finally to the design of a prototype of semi-passive walker

A Simulation Framework for Simultaneous Design and Control of Passivity Based Walkers

International audienceIn this paper, we propose a simulation framework which simultaneously computes both the design and the control of bipedal walkers. The problem of computing a design and a control is formulated as a single large-scale parametric optimal control problem on hybrid dynamics with path constraints (e.g. non sliding and non slipping contact constraints). Our framework relies on state-of-the-art numerical optimal control techniques and efficient computation of the multi-body rigid dynamics. It allows to compute both the parametrized model and the control of passive walkers on different scenarios, in only few seconds on a standard computer. The framework is illustrated by several examples which highlight the interest of the approach

Introducing Force Feedback in Model Predictive Control

International audienceIn the literature about model predictive control (MPC), contact forces are planned rather than controlled. In this paper, we propose a novel paradigm to incorporate effort measurements into a predictive controller, hence allowing to control them by direct measurement feedback. We first demonstrate why the classical optimal control formulation, based on position and velocity state feedback, cannot handle direct feedback on force information. Following previous approaches in force control, we then propose to augment the classical formulations with a model of the robot actuation, which naturally allows to generate online trajectories that adapt to sensed position, velocity and torques. We propose a complete implementation of this idea on the upper part of a real humanoid robot, and show through hardware experiments that this new formulation incorporating effort feedback outperforms classical MPC in challenging tasks where physical interaction with the environment is crucial

Introducing Force Feedback in Model Predictive Control

International audienceIn the literature about model predictive control (MPC), contact forces are planned rather than controlled. In this paper, we propose a novel paradigm to incorporate effort measurements into a predictive controller, hence allowing to control them by direct measurement feedback. We first demonstrate why the classical optimal control formulation, based on position and velocity state feedback, cannot handle direct feedback on force information. Following previous approaches in force control, we then propose to augment the classical formulations with a model of the robot actuation, which naturally allows to generate online trajectories that adapt to sensed position, velocity and torques. We propose a complete implementation of this idea on the upper part of a real humanoid robot, and show through hardware experiments that this new formulation incorporating effort feedback outperforms classical MPC in challenging tasks where physical interaction with the environment is crucial

Learning to steer a locomotion contact planner

International audienceThe combinatorics inherent to the issue of planning legged locomotion can be addressed by decomposing the problem: first, select a guide path abstracting the contacts with a heuristic models; then compute the contact sequence to balance the robot gait along the guide path. While several models have been proposed to compute such path, none have yet managed to efficiently capture the complexity of legged locomotion on arbitrary terrain. In this paper, we present a novel method to automatically build a local controller, or steering method, able to generate a guide path along which a feasible contact sequence can be built. Our reinforcement learning approach is coupled with a geometric condition for feasibility during the training, which improves the convergence rate without inducing a loss in generality. We have designed a dedicated environment and the associated reward function where a classical reinforcement learning algorithm can be run to compute the steering method. The policy takes as inputs a target direction and a local heightmap of the terrain around the robot, to steer the path where new contacts should be created. It is then coupled with a contact generator that creates the contacts to support the robot movement. We demonstrate that the trained policy is able to generate feasible contact plans with a higher success rates than previous approaches and that it generalises to terrains not considered during the training. As a result, the policy can be used with a path planning algorithm to navigate in complex environments

Torque Controlled Locomotion of a Biped Robot with Link Flexibility

International audienceWhen a big and heavy robot moves, it exerts large forces on the environment and on its own structure, its angular momentum can varysubstantially, and even the robot's structure can deform if there is a mechanical weakness. Under these conditions, standard locomotion controllers can fail easily. In this article, we propose a complete control scheme to work with heavy robots in torque control. The full centroidal dynamics is used to generate walking gaits online, link deflections are taken into account to estimate the robot posture and all postural instructions are designed to avoid conflicting with each other, improving balance. These choices reduce model and control errors, allowing our centroidal stabilizer to compensate for the remaining residual errors. The stabilizer and motion generator are designed together to ensure feasibility under the assumption of bounded errors. We deploy this scheme to control the locomotion of the humanoid robot Talos, whose hip links flex when walking. It allows us to reach steps of 35~cm, for an average speed of 25~cm/sec, which is among the best performances so far for torque-controlled electric robots

The Pinocchio C++ library – A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives

International audienceWe introduce Pinocchio, an open-source software framework that implements rigid body dynamics algorithms and their analytical derivatives. Pinocchio does not only include standard algorithms employed in robotics (e.g., forward and inverse dynamics) but provides additional features essential for the control, the planning and the simulation of robots. In this paper, we describe these features and detail the programming patterns and design which make Pinocchio efficient. We evaluate the performances against RBDL, another framework with broad dissemination inside the robotics community. We also demonstrate how the source code generation embedded in Pinocchio outperforms other approaches of state of the art