Planning humanlike actions in blending spaces

Abstract—We introduce an approach for enabling sampling-based planners to compute motions with humanlike appearance. The proposed method is based on a space of blendable example motions collected by motion capture. This space is explored by a sampling-based planner that is able to produce motions around obstacles while keeping solutions similar to the original examples. The results therefore largely maintain the humanlike characteristics observed in the example motions. The method is applied to generic upper-body actions and is complemented by a locomotion planner that searches for suitable body placements for executing upper-body actions successfully. As a result, our overall multi-modal planning method is able to automatically coordinate whole-body motions for action execution among obstacles, and the produced motions remain similar to example motions given as input to the system. I

Motion Planning : from Digital Actors to Humanoid Robots

Le but de ce travail est de développer des algorithmes de planification de mouvement pour des figures anthropomorphes en tenant compte de la géométrie, de la cinématique et de la dynamique du mécanisme et de son environnement. Par planification de mouvement, on entend la capacité de donner des directives à un niveau élevé et de les transformer en instructions de bas niveau qui produiront une séquence de valeurs articulaires qui reproduissent les mouvements humains. Ces instructions doivent considérer l'évitement des obstacles dans un environnement qui peut être plus au moins contraint. Ceci a comme consequence que l'on peut exprimer des directives comme “porte ce plat de la table jusqu'ac'estu coin du piano”, qui seront ensuite traduites en une série de buts intermédiaires et de contraintes qui produiront les mouvements appropriés des articulations du robot, de façon a effectuer l'action demandée tout en evitant les obstacles dans la chambre. Nos algorithmes se basent sur l'observation que les humains ne planifient pas des mouvements précis pour aller à un endroit donné. On planifie grossièrement la direction de marche et, tout en avançant, on exécute les mouvements nécessaires des articulations afin de nous mener à l'endroit voulu. Nous avons donc cherché à concevoir des algorithmes au sein d'un tel paradigme, algorithmes qui: 1. Produisent un chemin sans collision avec une version réduite du mécanisme et qui le mènent au but spécifié. 2. Utilisent les contrôleurs disponibles pour générer un mouvement qui assigne des valeurs à chacune des articulations du mécanisme pour suivre le chemin trouvé précédemment. 3. Modifient itérativement ces trajectoires jusqu'à ce que toutes les contraintes géométriques, cinématiques et dynamiques soient satisfaites. Dans ce travail nous appliquons cette approche à trois étages au problème de la planification de mouvements pour des figures anthropomorphes qui manipulent des objets encombrants tout en marchant. Dans le processus, plusieurs problèmes intéressants, ainsi que des propositions pour les résoudre, sont présentés. Ces problèmes sont principalement l'évitement tri-dimensionnel des obstacles, la manipulation des objets à deux mains, la manipulation coopérative des objets et la combinaison de comportements hétérogènes. La contribution principale de ce travail est la modélisation du problème de la génération automatique des mouvements de manipulation et de locomotion. Ce modèle considère les difficultés exprimées ci dessus, dans les contexte de mécanismes bipèdes. Trois principes fondent notre modèle: une décomposition fonctionnelle des membres du mécanisme, un modèle de manipulation coopérative et, un modéle simplifié des facultés de déplacement du mécanisme dans son environnement.Ce travail est principalement et surtout, un travail de synthèse. Nous nous servons des techniques disponibles pour commander la locomotion des mécanismes bipèdes (contrôleurs) provenant soit de l'animation par ordinateur, soit de la robotique humanoïde, et nous les relions dans un planificateur des mouvements original. Ce planificateur de mouvements est agnostique vis-à-vis du contrôleur utilisé, c'est-à-dire qu'il est capable de produire des mouvements libres de collision avec n'importe quel contrôleur tandis que les entrées et sorties restent compatibles. Naturellement, l'exécution de notre planificateur dépend en grand partie de la qualité du contrôleur utilisé. Dans cette thèse, le planificateur de mouvement est relié à différents contrôleurs et ses bonnes performances sont validées avec des mécanismes différents, tant virtuels que physiques. Ce travail à été fait dans le cadre des projets de recherche communs entre la France, la Russie et le Japon, où nous avons fourni le cadre de planification de mouvement à ses différents contrôleurs. Plusieurs publications issues de ces collaborations ont été présentées dans des conférences internationales. Ces résultats sont compilés et présentés dans cette thèse, et le choix des techniques ainsi que les avantages et inconvénients de notre approche sont discutés. ABSTRACT : The goal of this work is to develop motion planning algorithms for human-like figures taking into account the geometry, kinematics and dynamics of the mechanism and its environment. By motion planning it is understood the ability to specify high-level directives and transform them into low-level instructions for the articulations of the human-like figure. This is usually done while considering obstacle avoidance within the environment. This results in one being able to express directives as “carry this plate from the table to the piano corner” and have them translate into a series of goals and constraints that result in the pertinent motions from the robot's articulations in such a way as to carry out the action while avoiding collisions with the obstacles in the room. Our algorithms are based on the observation that humans do not plan their exact motions when getting to a location. We roughly plan our direction and, as we advance, we execute the motions needed to get to the desired place. This has led us to design algorithms that: 1. Produce a rough collision free path that takes a simplified model of the mechanism to the desired location. 2. Use available controllers to generate a trajectory that assigns values to each of the mechanism's articulations to follow the path. 3. Modify iteratively these trajectories until all the geometric, kinematic and dynamic constraints of the problem are satisfied.Throughout this work, we apply this three-stage approach with the problem of generating motions for human-like figures that manipulate bulky objects while walking. In the process, several interesting problems and their solution are brought into focus. These problems are, three- imensional collision avoidance, two-hand object manipulation, cooperative manipulation among several characters or robots and the combination of different behaviors. The main contribution of this work is the modeling of the automatic generation of cooperative manipulation motions. This model considers the above difficulties, all in the context of bipedal walking mechanisms. Three principles inform the model: a functional decomposition of the mechanism's limbs, a model for cooperative manipulation and, a simplified model to represent the mechanism when generating the rough path. This work is mainly and above all, one of synthesis. We make use of available techniques for controlling locomotion of bipedal mechanisms (controllers), from the fields of computer graphics and robotics, and connect them to a novel motion planner. This motion planner is controller-agnostic, that is, it is able to produce collision-free motions with any controller, despite whatever errors introduced by the controller itself. Of course, the performance of our motion planner depends on the quality of the used controller. In this thesis, the motion planner, connected to different controllers, is used and tested in different mechanisms, both virtual and physical. This in the context of different research projects in France, Russia and Japan, where we have provided the motion planning framework to their controllers. Several papers in peer-reviewed international conferences have resulted from these collaborations. The present work compiles these results and provides a more comprehensive and detailed depiction of the system and its benefits, both when applied to different mechanisms and compared to alternative approache

Reset-free Trial-and-Error Learning for Robot Damage Recovery

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at https://youtu.be/IqtyHFrb3BU, code at https://github.com/resibots/chatzilygeroudis_2018_rt

Human Arm simulation for interactive constrained environment design

During the conceptual and prototype design stage of an industrial product, it is crucial to take assembly/disassembly and maintenance operations in advance. A well-designed system should enable relatively easy access of operating manipulators in the constrained environment and reduce musculoskeletal disorder risks for those manual handling operations. Trajectory planning comes up as an important issue for those assembly and maintenance operations under a constrained environment, since it determines the accessibility and the other ergonomics issues, such as muscle effort and its related fatigue. In this paper, a customer-oriented interactive approach is proposed to partially solve ergonomic related issues encountered during the design stage under a constrained system for the operator's convenience. Based on a single objective optimization method, trajectory planning for different operators could be generated automatically. Meanwhile, a motion capture based method assists the operator to guide the trajectory planning interactively when either a local minimum is encountered within the single objective optimization or the operator prefers guiding the virtual human manually. Besides that, a physical engine is integrated into this approach to provide physically realistic simulation in real time manner, so that collision free path and related dynamic information could be computed to determine further muscle fatigue and accessibility of a product designComment: International Journal on Interactive Design and Manufacturing (IJIDeM) (2012) 1-12. arXiv admin note: substantial text overlap with arXiv:1012.432

ADAPT: The Agent Development and Prototyping Testbed

We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. Our animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. Additionally, our navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a character’s animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model