Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

구조로봇을 위한 강건한 계층적 동작 계획 및 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 박종우.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions. We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels. In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration. We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization. Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner. Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.최근에 등장한 새로운 세대의 로봇은 기존에는 인간만이 할 수 있었던 복잡한 일을 로봇 또한 수행할 수 있음을 보여주었다. 특히 DARPA Robotics Challenge를 통해 이러한 사실을 잘 확인할 수 있으며, 이 로봇들은 공장과 같은 정형화된 환경에서 자동화된 일을 반복적으로 수행하던 임무에서 더 나아가 극한의 환경에서 인간을 대신하여 위험한 임무를 수행할 수 있는 방향으로 발전하고 있다. 그래서 사람들은 재난환경에서 안전하고 시의 적절하게 대응할 수 있는 여러 가지 대안 중에서 실현 가능성이 높은 대처 방안으로 로봇을 생각하게 되었다. 하지만 이러한 로봇은 동적으로 변화하는 비정형 환경에서 임무를 수행할 수 있어야 하기 때문에 불확실성에 대해 강건해야하고, 다양한 환경 조건에서 능동적으로 반응을 할 수 있어야 한다. 본 학위논문에서는 로봇이 비정형 환경에서 강건하면서도 적응적으로 동작할 수 있는 실시간 최적화 기반의 동작 계획 및 제어 방법과 구조 로봇의 설계 개념을 제안하고자 한다. 인간은 많은 자유도를 가지고 있으며, 하나의 전신 동작을 생성할 때 다양한 기구학 혹은 동역학적 특성을 가지는 세부 동작 혹은 작업을 정의하고, 이를 효과적으로 종합할 수 있다. 그리고 학습을 통해 각 동작 요소들을 최적화할 뿐만 아니라 상황 에 따라 각 동작 요소에 우선순위를 부여하여 이를 효과적으로 결합하거나 분리하여 실시간으로 최적의 동작을 생성하고 제어한다. 즉, 상황에 따라 중요한 동작요소를 우선적으로 수행하고 우선순위가 낮은 동작요소는 부분 혹은 전체적으로 포기하기도 하면서 매우 유연하게 전체 동작을 생성하고 최적화 한다. 인간과 같이 다자유도를 보유한 로봇 또한 기구학과 동역학적 특성을 가지는 다양한 세부 동작 혹은 작업을 작업공간(task space) 혹은 관절공간(configuration space)에서 정의할 수 있으며, 우선순위에 따라 이를 효과적으로 결합하여 전체 동작을 생 성하고 제어할 수 있다. 서로 양립하기 어려운 로봇의 동작 문제를 해결하기 위해 동작들 사이에 우선순위를 부여하여 계층을 생성하고, 이에 따라 로봇의 전신 동작을 구현하는 방법은 오랫동안 연구가 진행되어 왔다. 이러한 계층적 최적화를 이용하면 우선순위가 높은 동작부터 순차적으로 실행하지만, 우선순위가 낮은 동작요소들도 가능한 만족시키는 최적의 해를 찾을 수 있다. 하지만 관절의 구동 범위와 같은 부등식의 조건이 포함된 계층적 최적화 문제에서 특이점에 대한 강건성까지 확보할 수 있는 방법에 대해서는 아직까지 많은 부분이 밝 혀진 바가 없다. 따라서 본 학위논문에서는 등식과 부등식으로 표현되는 구속조건 혹은 동작요소를 계층적 최적화에 동시에 포함시키고, 특이점이 존재하더라도 강건성과 수렴성을 보장하는 관절공간에서의 최적해를 확보하는데 집중한다. 왜나하면 비정형 임무를 수행하는 로봇은 사전에 계획된 동작을 수행하는 것이 아닌 변화하는 환경조건에 따라 실시간으로 동작을 계획하고 제어해야 하기 때문에 특이점이 없는 자세로 로봇을 항상 제어하기가 어렵다. 그리고 이렇게 특이점을 회피하는 방향으로 로봇을 제어하는 것은 로봇의 운용성을 심각하게 저해시킬 수 있다. 특이점 근방에서의 해의 강건성이 보장되지 않으면 로봇 관절에 과도한 속도 혹은 토크가 발생하여 로봇의 임무 수행이 불가능하거나 환경과 로봇의 손상을 초래할 수 있으며, 나아가 로봇과 함께 임무를 수행하는 사람에게 상해를 가할 수도 있다. 특이점에 대한 강건성을 확보하기 위해 우선순위 기반의 계층적 최적화와 정규화 (regularization)를 통합하여 정규화된 계층적 최적화 (RHQP: Regularized Hierarchical Quadratic Program) 문제를 다룬다. 부등식이 포함된 계층적 최적화에 정규화를 동시에 고려함으로써 야기되는 많은 문제점들을 해결하고 해의 최적성과 강건성을 확보할 수 있는 방법을 제안한다. 특히 외부의 최적화 프로그램을 사용하지 않고 수치적 최적화 (numerical optimization) 이론과 우선순위에 기반을 두는 여유자유도 로봇의 해석 기법을 이용하여 계산의 효율성을 극대화할 수 있는 이차 프로그램(quadratic programming)을 제안한다. 또한 이와 동시에 정규화된 계층적 최적화 문제의 이론적 구조를 철저하게 분석한다. 특히 특이값 분해 (singular value decomposition)를 통해 최적해와 부등식 조건을 처리하는데 필요한 라그랑지 승수를 재귀적인 방법으로 해석적 형태로 구함으로써 계산의 효율성을 증대시키고 동시에 부등식의 조건을 오류 없이 정확하게 처리할 수 있도록 하였다. 그리고 정규화된 계층적 최적화를 힘제어까지 확장하여 환경과 로봇의 안전한 상호작용을 보장하여 로봇이 적절한 힘으로 환경과 접촉할 수 있도록 하였다. 불확실성이 존재하는 비정형 환경에서 비정형 임무를 수행할 수 있는 구조로봇의 핵심 설계 개념을 제시한다. 비정형 환경에서의 조작 성능과 이동 성능을 동시에 확보할 수 있는 형상으로 로봇을 설계하여 구조 로봇으로 하여금 최종 목적으로 설정된 인간을 대신하여 부상자를 구조하고 위험물을 처리하는 임무를 효과적으로 수행할 수 있도록 한다. 구조 로봇에 필요한 매니퓰레이터는 부상자 구조 임무와 위험물 처리 임무에 따라 교체 가능한 모듈형으로 설계하여 각각의 임무에 따라 최적화된 매니퓰 레이터를 장착하여 임무를 수행할 수 있다. 하체는 트랙과 관절이 결합된 하이브리드 형태를 취하고 있으며, 주행 임무와 조작임무에 따라 형상을 변경할 수 있다. 형상 변경과 모듈화된 매니퓰레이터를 통해서조작 성능과 험한 지형에서 이동할 수 있는 주행 성능을 동시에 확보하였다. 최종적으로 구조로봇의 설계와 실시간 계층적 제어를 이용하여 비정형 실내외 환경에서 구조로봇이 주행임무, 위험물 조작임무, 부상자 구조 임무를 성공적으로 수 행할 수 있음을 해석과 실험을 통하여 입증함으로써 본 학위논문에서 제안한 설계와 정규화된 계층적 최적화 기반의 제어 전략의 유용성을 검증하였다.1 Introduction 1 1.1 Motivations 1 1.2 Related Works and Research Problems for Hierarchical Control 3 1.2.1 Classical Approaches 3 1.2.2 State-of-the-Art Strategies 4 1.2.3 Research Problems 7 1.3 Robust Rescue Robots 9 1.4 Research Goals 12 1.5 Contributions of ThisThesis 13 1.5.1 Robust Hierarchical Task-Priority Control 13 1.5.2 Design Concepts of Robust Rescue Robot 16 1.5.3 Hierarchical Motion and ForceControl 17 1.6 Dissertation Preview 18 2 Preliminaries for Task-Priority Control Framework 21 2.1 Introduction 21 2.2 Task-Priority Inverse Kinematics 23 2.3 Recursive Formulation of Null Space Projector 28 2.4 Conclusion 31 3 Robust Hierarchical Task-Priority Control 33 3.1 Introduction 33 3.1.1 Motivations 35 3.1.2 Objectives 36 3.2 Task Function Approach 37 3.3 Regularized Hierarchical Optimization with Equality Tasks 41 3.3.1 Regularized Hierarchical Optimization 41 3.3.2 Optimal Solution 45 3.3.3 Task Error and Hierarchical Matrix Decomposition 49 3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56 3.4 Regularized Hierarchical Optimization with Inequality Constraints 60 3.4.1 Lagrange Multipliers 61 3.4.2 Modified Active Set Method 66 3.4.3 Illustrative Examples of Modified Active Set Method 70 3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72 3.5 DLS-HQP Algorithm 79 3.6 Concluding Remarks 80 4 Rescue Robot Design and Experimental Results 83 4.1 Introduction 83 4.2 Rescue Robot Design 85 4.2.1 System Design 86 4.2.2 Variable Configuration Mobile Platform 92 4.2.3 Dual Arm Manipulators 95 4.2.4 Software Architecture 97 4.3 Performance Verification for Hierarchical Motion Control 99 4.3.1 Real-Time Motion Generation 99 4.3.2 Task Specifications 103 4.3.3 Singularity Robust Task Priority 106 4.3.4 Inequality Constraint Handling and Computation Time 111 4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117 4.5 Field Tests 122 4.6 Concluding Remarks 126 5 Hierarchical Motion and Force Control 129 5.1 Introduction 129 5.2 Operational Space Control 132 5.3 Acceleration-Based Hierarchical Motion Control 134 5.4 Force Control 137 5.4.1 Force Control with Inner Position Loop 141 5.4.2 Force Control with Inner Velocity Loop 144 5.5 Motion and Force Control 145 5.6 Numerical Results for Acceleration-Based Motion and Force Control 148 5.6.1 Task Specifications 150 5.6.2 Force Control Performance 151 5.6.3 Singularity Robustness and Inequality Constraint Handling 155 5.7 Velocity Resolved Motion and Force Control 160 5.7.1 Velocity-Based Motion and Force Control 161 5.7.2 Experimental Results 163 5.8 Concluding Remarks 167 6 Conclusion 169 6.1 Summary 169 6.2 Concluding Remarks 173 A Appendix 175 A.1 Introduction to PID Control 175 A.2 Inverse Optimal Control 176 A.3 Experimental Results and Conclusion 181 Bibliography 183 Abstract 207박

Visual guidance of unmanned aerial manipulators

The ability to fly has greatly expanded the possibilities for robots to perform surveillance, inspection or map generation tasks. Yet it was only in recent years that research in aerial robotics was mature enough to allow active interactions with the environment. The robots responsible for these interactions are called aerial manipulators and usually combine a multirotor platform and one or more robotic arms. The main objective of this thesis is to formalize the concept of aerial manipulator and present guidance methods, using visual information, to provide them with autonomous functionalities. A key competence to control an aerial manipulator is the ability to localize it in the environment. Traditionally, this localization has required external infrastructure of sensors (e.g., GPS or IR cameras), restricting the real applications. Furthermore, localization methods with on-board sensors, exported from other robotics fields such as simultaneous localization and mapping (SLAM), require large computational units becoming a handicap in vehicles where size, load, and power consumption are important restrictions. In this regard, this thesis proposes a method to estimate the state of the vehicle (i.e., position, orientation, velocity and acceleration) by means of on-board, low-cost, light-weight and high-rate sensors. With the physical complexity of these robots, it is required to use advanced control techniques during navigation. Thanks to their redundancy on degrees-of-freedom, they offer the possibility to accomplish not only with mobility requirements but with other tasks simultaneously and hierarchically, prioritizing them depending on their impact to the overall mission success. In this work we present such control laws and define a number of these tasks to drive the vehicle using visual information, guarantee the robot integrity during flight, and improve the platform stability or increase arm operability. The main contributions of this research work are threefold: (1) Present a localization technique to allow autonomous navigation, this method is specifically designed for aerial platforms with size, load and computational burden restrictions. (2) Obtain control commands to drive the vehicle using visual information (visual servo). (3) Integrate the visual servo commands into a hierarchical control law by exploiting the redundancy of the robot to accomplish secondary tasks during flight. These tasks are specific for aerial manipulators and they are also provided. All the techniques presented in this document have been validated throughout extensive experimentation with real robotic platforms.La capacitat de volar ha incrementat molt les possibilitats dels robots per a realitzar tasques de vigilància, inspecció o generació de mapes. Tot i això, no és fins fa pocs anys que la recerca en robòtica aèria ha estat prou madura com per començar a permetre interaccions amb l’entorn d’una manera activa. Els robots per a fer-ho s’anomenen manipuladors aeris i habitualment combinen una plataforma multirotor i un braç robòtic. L’objectiu d’aquesta tesi és formalitzar el concepte de manipulador aeri i presentar mètodes de guiatge, utilitzant informació visual, per dotar d’autonomia aquest tipus de vehicles. Una competència clau per controlar un manipulador aeri és la capacitat de localitzar-se en l’entorn. Tradicionalment aquesta localització ha requerit d’infraestructura sensorial externa (GPS, càmeres IR, etc.), limitant així les aplicacions reals. Pel contrari, sistemes de localització exportats d’altres camps de la robòtica basats en sensors a bord, com per exemple mètodes de localització i mapejat simultànis (SLAM), requereixen de gran capacitat de còmput, característica que penalitza molt en vehicles on la mida, pes i consum elèctric son grans restriccions. En aquest sentit, aquesta tesi proposa un mètode d’estimació d’estat del robot (posició, velocitat, orientació i acceleració) a partir de sensors instal·lats a bord, de baix cost, baix consum computacional i que proporcionen mesures a alta freqüència. Degut a la complexitat física d’aquests robots, és necessari l’ús de tècniques de control avançades. Gràcies a la seva redundància de graus de llibertat, aquests robots ens ofereixen la possibilitat de complir amb els requeriments de mobilitat i, simultàniament, realitzar tasques de manera jeràrquica, ordenant-les segons l’impacte en l’acompliment de la missió. En aquest treball es presenten aquestes lleis de control, juntament amb la descripció de tasques per tal de guiar visualment el vehicle, garantir la integritat del robot durant el vol, millorar de l’estabilitat del vehicle o augmentar la manipulabilitat del braç. Aquesta tesi es centra en tres aspectes fonamentals: (1) Presentar una tècnica de localització per dotar d’autonomia el robot. Aquest mètode està especialment dissenyat per a plataformes amb restriccions de capacitat computacional, mida i pes. (2) Obtenir les comandes de control necessàries per guiar el vehicle a partir d’informació visual. (3) Integrar aquestes accions dins una estructura de control jeràrquica utilitzant la redundància del robot per complir altres tasques durant el vol. Aquestes tasques son específiques per a manipuladors aeris i també es defineixen en aquest document. Totes les tècniques presentades en aquesta tesi han estat avaluades de manera experimental amb plataformes robòtiques real

Extension of the Control Concept for a Mobile Overhead Manipulator to Whole-Body Impedance Control

At present, robots constitute a central component of contemporary factories. The application of traditional ground-based systems, however, may lead to congested floors with minimal space left for new robots or human workers. Overhead manipulators, on the other hand, aim to occupy the unutilized ceiling space, in order to manipulate the workspace located below them. The SwarmRail system is an example of such an overhead manipulator. This concept deploys mobile units driving across a passive railstructure above the ground. Additionally, equipping the mobile units with robotic arms at their bottom side enables this design to provide continuous overhead manipulation while in motion. Although a first demonstrator confirmed the functional capability of said system, the current hardware suffers from complications while traversing rail crossings. Due to uneven rails consecutive rails, said crossing points cause the robot's wheels to collide with the new rail segment it is driving towards. Additionally, the robot experiences an undesired sudden altitude change. In this thesis, we aim to implement a hierarchical whole-body impedance tracking controller for the robots employed within the SwarmRail system. Our controller combines a kinematically controlled mobile unit with the impedance-based control of a robotic arm through an admittance interface. The focus of this thesis is set on the controller's robustness against the previously mentioned external disturbances. The performance of this controller is validated inside a simulation that incorporates the aforementioned complications. Our findings suggest, that the control strategy presented in this thesis provides a foundation for the development of a controller applicable to the physical demonstrator

Ein hierarchisches Framework für physikalische Mensch-Roboter-Interaktion

Robots are becoming more than machines performing repetitive tasks behind safety fences, and are expected to perform multiple complex tasks and work together with a human. For that purpose, modern robots are commonly built with two main characteristics: a large number of joints to increase their versatility and the capability to feel the environment through torque/force sensors. Controlling such complex robots requires the development of sophisticated frameworks capable of handling multiple tasks. Various frameworks have been proposed in the last years to deal with the redundancy caused by a large number of joints. Those hierarchical frameworks prioritize the achievement of the main task with the whole robot capability, while secondary tasks are performed as well as the remaining mobility allows it. This methodology presents considerable drawbacks in applications requiring that the robot respects constraints imposed by, e.g., safety restrictions or physical limitations. One particular case is unilateral constraints imposed by, e.g., joint or workspace limits. To implement them, task hierarchical frameworks rely on the activation of repulsive potential fields when approaching the limit. The performance of the potential field depends on the configuration and speed of the robot. Additionally, speed limitation is commonly required in collaborative scenarios, but it has been insufficiently treated for torque-controlled robots. With the aim of controlling redundant robots in collaborative scenarios, this thesis proposes a framework that handles multiple tasks under multiple constraints. The robot’s reaction to physical interaction must be intuitive and safe for humans: The robot must not impose high forces on the human or react unexpectedly to external forces. The proposed framework uses novel methods to avoid exceeding position, velocity and acceleration limits in joint and Cartesian spaces. A comparative study is carried out between different redundancy resolution solvers to contrast the diverse approaches used to solve the redundancy problem. Widely used projector-based and quadratic programming-based hierarchical solvers were experimentally analyzed when reacting to external forces. Experiments were performed using an industrial redundant collaborative robot. Results demonstrate that the proposed method to handle unilateral constraints produces a safe and expected reaction in the presence of external forces exerted by humans. The robot does not exceed the given limits, while the tasks performed are prioritized hierarchically

Generating whole body movements for dynamics anthropomorphic systems under constraints

Cette thèse étudie la question de la génération de mouvements corps-complet pour des systèmes anthropomorphes. Elle considère le problème de la modélisation et de la commande en abordant la question difficile de la génération de mouvements ressemblant à ceux de l'homme. En premier lieu, un modèle dynamique du robot humanoïde HRP-2 est élaboré à partir de l'algorithme récursif de Newton-Euler pour les vecteurs spatiaux. Un nouveau schéma de commande dynamique est ensuite développé, en utilisant une cascade de programmes quadratiques (QP) optimisant des fonctions coûts et calculant les couples de commande en satisfaisant des contraintes d'égalité et d'inégalité. La cascade de problèmes quadratiques est définie par une pile de tâches associée à un ordre de priorité. Nous proposons ensuite une formulation unifiée des contraintes de contacts planaires et nous montrons que la méthode proposée permet de prendre en compte plusieurs contacts non coplanaires et généralise la contrainte usuelle du ZMP dans le cas où seulement les pieds sont en contact avec le sol. Nous relions ensuite les algorithmes de génération de mouvement issus de la robotique aux outils de capture du mouvement humain en développant une méthode originale de génération de mouvement visant à imiter le mouvement humain. Cette méthode est basée sur le recalage des données capturées et l'édition du mouvement en utilisant le solveur hiérarchique précédemment introduit et la définition de tâches et de contraintes dynamiques. Cette méthode originale permet d'ajuster un mouvement humain capturé pour le reproduire fidèlement sur un humanoïde en respectant sa propre dynamique. Enfin, dans le but de simuler des mouvements qui ressemblent à ceux de l'homme, nous développons un modèle anthropomorphe ayant un nombre de degrés de liberté supérieur à celui du robot humanoïde HRP2. Le solveur générique est utilisé pour simuler le mouvement sur ce nouveau modèle. Une série de tâches est définie pour décrire un scénario joué par un humain. Nous montrons, par une simple analyse qualitative du mouvement, que la prise en compte du modèle dynamique permet d'accroitre naturellement le réalisme du mouvement.This thesis studies the question of whole body motion generation for anthropomorphic systems. Within this work, the problem of modeling and control is considered by addressing the difficult issue of generating human-like motion. First, a dynamic model of the humanoid robot HRP-2 is elaborated based on the recursive Newton-Euler algorithm for spatial vectors. A new dynamic control scheme is then developed adopting a cascade of quadratic programs (QP) optimizing the cost functions and computing the torque control while satisfying equality and inequality constraints. The cascade of the quadratic programs is defined by a stack of tasks associated to a priority order. Next, we propose a unified formulation of the planar contact constraints, and we demonstrate that the proposed method allows taking into account multiple non coplanar contacts and generalizes the common ZMP constraint when only the feet are in contact with the ground. Then, we link the algorithms of motion generation resulting from robotics to the human motion capture tools by developing an original method of motion generation aiming at the imitation of the human motion. This method is based on the reshaping of the captured data and the motion editing by using the hierarchical solver previously introduced and the definition of dynamic tasks and constraints. This original method allows adjusting a captured human motion in order to reliably reproduce it on a humanoid while respecting its own dynamics. Finally, in order to simulate movements resembling to those of humans, we develop an anthropomorphic model with higher number of degrees of freedom than the one of HRP-2. The generic solver is used to simulate motion on this new model. A sequence of tasks is defined to describe a scenario played by a human. By a simple qualitative analysis of motion, we demonstrate that taking into account the dynamics provides a natural way to generate human-like movements

A Comparative Experimental Study of Multi-Tasking Tracking and Interaction Control on a Torque-Controlled Humanoid Robot

Multi-tasking control exploits kinematic redundancy of robots to attain several control objectives at the same time. To properly coordinate the subtasks according to their importance, they are usually stacked into a prioritized hierarchy. In this work, two passivity-based multi-tasking control strategies developed in our recent work that feature strict prioritization and mathematically proved stability properties, are experimentally compared with a state-of-the-art method using feedback linearization on a torque-controlled humanoid robot. The conducted experimental study aims at providing insights into the practical properties of the controllers in real-world scenarios whence the robot has to execute a mixture of trajectory tracking and physical interaction tasks