A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks

Exploiting interaction with the environment is a promising and powerful way to enhance stability of humanoid robots and robustness while executing locomotion and manipulation tasks. Recently some works have started to show advances in this direction considering humanoid locomotion with multi-contacts, but to be able to fully develop such abilities in a more autonomous way, we need to first understand and classify the variety of possible poses a humanoid robot can achieve to balance. To this end, we propose the adaptation of a successful idea widely used in the field of robot grasping to the field of humanoid balance with multi-contacts: a whole-body pose taxonomy classifying the set of whole-body robot configurations that use the environment to enhance stability. We have revised criteria of classification used to develop grasping taxonomies, focusing on structuring and simplifying the large number of possible poses the human body can adopt. We propose a taxonomy with 46 poses, containing three main categories, considering number and type of supports as well as possible transitions between poses. The taxonomy induces a classification of motion primitives based on the pose used for support, and a set of rules to store and generate new motions. We present preliminary results that apply known segmentation techniques to motion data from the KIT whole-body motion database. Using motion capture data with multi-contacts, we can identify support poses providing a segmentation that can distinguish between locomotion and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots and System

Analyzing Whole-Body Pose Transitions in Multi-Contact Motions

When executing whole-body motions, humans are able to use a large variety of support poses which not only utilize the feet, but also hands, knees and elbows to enhance stability. While there are many works analyzing the transitions involved in walking, very few works analyze human motion where more complex supports occur. In this work, we analyze complex support pose transitions in human motion involving locomotion and manipulation tasks (loco-manipulation). We have applied a method for the detection of human support contacts from motion capture data to a large-scale dataset of loco-manipulation motions involving multi-contact supports, providing a semantic representation of them. Our results provide a statistical analysis of the used support poses, their transitions and the time spent in each of them. In addition, our data partially validates our taxonomy of whole-body support poses presented in our previous work. We believe that this work extends our understanding of human motion for humanoids, with a long-term objective of developing methods for autonomous multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion

We describe a whole-body dynamic walking controller implemented as a convex quadratic program. The controller solves an optimal control problem using an approximate value function derived from a simple walking model while respecting the dynamic, input, and contact constraints of the full robot dynamics. By exploiting sparsity and temporal structure in the optimization with a custom active-set algorithm, we surpass the performance of the best available off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We describe applications to balancing and walking tasks using the simulated Atlas robot in the DARPA Virtual Robotics Challenge.Comment: 6 pages, published at ICRA 201

Dynamic whole-body motion generation under rigid contacts and other unilateral constraints

The most widely used technique for generating wholebody motions on a humanoid robot accounting for various tasks and constraints is inverse kinematics. Based on the task-function approach, this class of methods enables the coordination of robot movements to execute several tasks in parallel and account for the sensor feedback in real time, thanks to the low computation cost. To some extent, it also enables us to deal with some of the robot constraints (e.g., joint limits or visibility) and manage the quasi-static balance of the robot. In order to fully use the whole range of possible motions, this paper proposes extending the task-function approach to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables. The definition of multiple objectives is made possible by ordering them inside a strict hierarchy. Several models of contact with the environment can be implemented in the framework. We propose a reduced formulation of the multiple rigid planar contact that keeps a low computation cost. The efficiency of this approach is illustrated by presenting several multicontact dynamic motions in simulation and on the real HRP-2 robot

Analyzing Whole-Body Pose Transitions in Multi-Contact Motions

Abstract-When executing whole-body motions, humans are able to use a large variety of support poses which not only utilize the feet, but also hands, knees and elbows to enhance stability. While there are many works analyzing the transitions involved in walking, very few works analyze human motion where more complex supports occur. In this work, we analyze complex support pose transitions in human motion involving locomotion and manipulation tasks (loco-manipulation). We have applied a method for the detection of human support contacts from motion capture data to a largescale dataset of loco-manipulation motions involving multicontact supports, providing a semantic representation of them. Our results provide a statistical analysis of the used support poses, their transitions and the time spent in each of them. In addition, our data partially validates our taxonomy of wholebody support poses presented in our previous work. We believe that this work extends our understanding of human motion for humanoids, with a long-term objective of developing methods for autonomous multi-contact motion planning

Déplacement d'un mannequin virtuel dans un environnement encombré : simulation de mouvement en intégrant les contraintes d'équilibre

This thesis was carried out in collaboration and co-funding of LSI of CEA/LIST and LBMC of IFSTTAR. The aim of the thesis was to study and develop a method for simulating the movement of a virtual manikin (VM) in a cluttered environment based on a priori knowledge. This thesis presents firstly motion capture (MoCap) experiments. The recorded data were analyzed to define some principles on human motion in cluttered environments. We then propose a general balance criterion and stability margin, based on a simplified model of VM. Then, we present a hierarchical framework that can generate and simulate dynamic movements of VM in a cluttered environment in three stages: a global trajectory of the center of mass (CoM) is generated at the global level to ensure balance in the VM's motion; then the trajectories of end-effectors (EE, ie feet, hands) and postures are generated locally under constraints of kinematics and collision avoidance; finally at the execution level, trajectories (CoM and EEs) and postures are used as references in a dynamic controller associated with VM so that the VM realizes the motion in a simulation. This framework is implemented in a car-ingress scenario in order to evaluate its performance and to suggest future improvementsCette thèse a été réalisée en collaboration et cofinancement impliquant le LSI du CEA/LIST et le LBMC de l'IFSTTAR. L'objectif de thèse était d'étudier et de développer une méthode pour simuler les mouvements d'un mannequin virtuel (MV) dans un environnement encombré en s'appuyant sur des connaissances a priori. L'étude présente, dans un premier temps, des expériences de capture de mouvement (MoCap). Les données enregistrées ont été analysées afin de définir quelques principes sur les mouvements humains dans des environnements encombrés. Nous proposons ensuite un critère général d'équilibre et une marge de stabilité, sur la base d'un modèle simplifié du MV. Puis, nous présentons un framework hiérarchique pouvant générer et simuler des mouvements dynamiques du MV dans un environnement encombré en trois étapes : une trajectoire globale du centre de masse (CoM) est générée au niveau global afin d'assurer l'équilibre du MV durant son mouvement; puis au niveau local, les trajectoires des organes terminaux (OT, i.e. pieds, mains) et les postures sont générées localement sous des contraintes cinématiques et d'évitement de collisions; enfin au niveau de l'exécution, les trajectoires (CoM et OTs) et les postures sont utilisées comme références dans un contrôleur dynamique associé au MV. Enfin, ce framework est mis en œuvre dans un scenario d'entrée dans un véhicule pour évaluer ses performances et proposer des améliorations future

Motion Primitives and Planning for Robots with Closed Chain Systems and Changing Topologies

When operating in human environments, a robot should use predictable motions that allow humans to trust and anticipate its behavior. Heuristic search-based planning offers predictable motions and guarantees on completeness and sub-optimality of solutions. While search-based planning on motion primitive-based (lattice-based) graphs has been used extensively in navigation, application to high-dimensional state-spaces has, until recently, been thought impractical. This dissertation presents methods we have developed for applying these graphs to mobile manipulation, specifically for systems which contain closed chains. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees-of-freedom but adds complexity in terms of constraints in the high-dimensional state-space. We exploit the dimensionality reduction inherent in closed kinematic chains to get efficient search-based planning. Our planner handles changing topologies (switching between open and closed-chains) in a single plan, including what transitions to include and when to include them. Thus, we can leverage existing results for search-based planning for open chains, combining open and closed chain manipulation planning into one framework. Proofs regarding the framework are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We apply this framework to planning for opening and navigating through non-spring and spring-loaded doors using a Willow Garage PR2. The framework motivates our approaches to the Atlas humanoid robot from Boston Dynamics for both stationary manipulation and quasi-static walking, as a closed chain is formed when both feet are on the ground