318 research outputs found
Learning body models: from humans to humanoids
Humans and animals excel in combining information from multiple sensory
modalities, controlling their complex bodies, adapting to growth, failures, or
using tools. These capabilities are also highly desirable in robots. They are
displayed by machines to some extent. Yet, the artificial creatures are lagging
behind. The key foundation is an internal representation of the body that the
agent - human, animal, or robot - has developed. The mechanisms of operation of
body models in the brain are largely unknown and even less is known about how
they are constructed from experience after birth. In collaboration with
developmental psychologists, we conducted targeted experiments to understand
how infants acquire first "sensorimotor body knowledge". These experiments
inform our work in which we construct embodied computational models on humanoid
robots that address the mechanisms behind learning, adaptation, and operation
of multimodal body representations. At the same time, we assess which of the
features of the "body in the brain" should be transferred to robots to give
rise to more adaptive and resilient, self-calibrating machines. We extend
traditional robot kinematic calibration focusing on self-contained approaches
where no external metrology is needed: self-contact and self-observation.
Problem formulation allowing to combine several ways of closing the kinematic
chain simultaneously is presented, along with a calibration toolbox and
experimental validation on several robot platforms. Finally, next to models of
the body itself, we study peripersonal space - the space immediately
surrounding the body. Again, embodied computational models are developed and
subsequently, the possibility of turning these biologically inspired
representations into safe human-robot collaboration is studied.Comment: 34 pages, 5 figures. Habilitation thesis, Faculty of Electrical
Engineering, Czech Technical University in Prague (2021
Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion
The estimation of external joint torque and contact wrench is essential for
achieving stable locomotion of humanoids and safety-oriented robots. Although
the contact wrench on the foot of humanoids can be measured using a
force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and
failure possibility of the system. This paper introduces a method for learning
external joint torque solely using proprioceptive sensors (encoders and IMUs)
for a floating base robot. For learning, the GRU network is used and random
walking data is collected. Real robot experiments demonstrate that the network
can estimate the external torque and contact wrench with significantly smaller
errors compared to the model-based method, momentum observer (MOB) with
friction modeling. The study also validates that the estimated contact wrench
can be utilized for zero moment point (ZMP) feedback control, enabling stable
walking. Moreover, even when the robot's feet and the inertia of the upper body
are changed, the trained network shows consistent performance with a
model-based calibration. This result demonstrates the possibility of removing
FTS on the robot, which reduces the disadvantages of hardware sensors. The
summary video is available at https://youtu.be/gT1D4tOiKpo.Comment: Accepted by 2023 IROS conferenc
Kontextsensitive Kรถrperregulierung fรผr redundante Roboter
In the past few decades the classical 6 degrees of freedom manipulators' dominance has been challenged by the rise of 7 degrees of freedom redundant robots. Similarly, with increased availability of humanoid robots in academic research, roboticists suddenly have access to highly dexterous platforms with multiple kinematic chains capable of undertaking multiple tasks simultaneously. The execution of lower-priority tasks, however, are often done in task/scenario specific fashion. Consequently, these systems are not scalable and slight changes in the application often implies re-engineering the entire control system and deployment which impedes the development process over time.
This thesis introduces an alternative systematic method of addressing the secondary tasks and redundancy resolution called, context aware body regulation. Contexts consist of one or multiple tasks, however, unlike the conventional definitions, the tasks within a context are not rigidly defined and maintain some level of abstraction. For instance, following a particular trajectory constitutes a concrete task while performing a Cartesian motion with the end-effector represents an abstraction of the same task and is more appropriate for context formulation. Furthermore, contexts are often made up of multiple abstract tasks that collectively describe a reoccurring situation. Body regulation is an umbrella term for a collection of schemes for addressing the robots' redundancy when a particular context occurs.
Context aware body regulation offers several advantages over traditional methods. Most notably among them are reusability, scalability and composability of contexts and body regulation schemes. These three fundamental concerns are realized theoretically by in-depth study and through mathematical analysis of contexts and regulation strategies; and are practically implemented by a component based software architecture that complements the theoretical aspects.
The findings of the thesis are applicable to any redundant manipulator and humanoids, and allow them to be used in real world applications. Proposed methodology presents an alternative approach for the control of robots and offers a new perspective for future deployment of robotic solutions.Im Verlauf der letzten Jahrzehnte wich der Einfluss klassischer Roboterarme mit 6 Freiheitsgraden zunehmend denen neuer und vielfรคltigerer Manipulatoren mit 7 Gelenken. Ebenso stehen der Forschung mit den neuartigen Humanoiden inzwischen auch hoch-redundante Roboterplattformen mit mehreren kinematischen Ketten zur Verfรผgung. Diese รผberaus flexiblen und komplexen Roboter-Kinematiken ermรถglichen generell das gleichzeitige Verfolgen mehrerer priorisierter Bewegungsaufgaben. Die Steuerung der weniger wichtigen Aufgaben erfolgt jedoch oft in anwendungsspezifischer Art und Weise, welche die Skalierung der Regelung zu generellen Kontexten verhindert. Selbst kleine รnderungen in der Anwendung bewirken oft schon, dass groรe Teile der Robotersteuerung รผberarbeitet werden mรผssen, was wiederum den gesamten Entwicklungsprozess behindert.
Diese Dissertation stellt eine alternative, systematische Methode vor um die Redundanz neuer komplexer Robotersysteme zu bewรคltigen und vielfรคltige, priorisierte Bewegungsaufgaben parallel zu steuern: Die so genannte kontextsensitive Kรถrperregulierung. Darin bestehen Kontexte aus einer oder mehreren Bewegungsaufgaben. Anders als in konventionellen Anwendungen sind die Aufgaben nicht fest definiert und beinhalten eine gewisse Abstraktion. Beispielsweise stellt das Folgen einer bestimmten Trajektorie eine sehr konkrete Bewegungsaufgabe dar, wรคhrend die Ausfรผhrung einer Kartesischen Bewegung mit dem Endeffektor eine Abstraktion darstellt, die fรผr die Kontextformulierung besser geeignet ist. Kontexte setzen sich oft aus mehreren solcher abstrakten Aufgaben zusammen und beschreiben kollektiv eine sich wiederholende Situation.
Durch die Verwendung der kontextsensitiven Kรถrperregulierung ergeben sich vielfรคltige Vorteile gegenรผber traditionellen Methoden: Wiederverwendbarkeit, Skalierbarkeit, sowie Komponierbarkeit von Konzepten. Diese drei fundamentalen Eigenschaften werden in der vorliegenden Arbeit theoretisch mittels grรผndlicher mathematischer Analyse aufgezeigt und praktisch mittels einer auf Komponenten basierenden Softwarearchitektur realisiert.
Die Ergebnisse dieser Dissertation lassen sich auf beliebige redundante Manipulatoren oder humanoide Roboter anwenden und befรคhigen diese damit zur realen Anwendung auรerhalb des Labors. Die hier vorgestellte Methode zur Regelung von Robotern stellt damit eine neue Perspektive fรผr die zukรผnftige Entwicklung von robotischen Lรถsungen dar
Integrating Vision and Physical Interaction for Discovery, Segmentation and Grasping of Unknown Objects
In dieser Arbeit werden Verfahren der Bildverarbeitung und die Fรคhigkeit
humanoider Roboter, mit ihrer Umgebung physisch zu interagieren, in engem
Zusammenspiel eingesetzt, um unbekannte Objekte zu identifizieren, sie vom
Hintergrund und anderen Objekten zu trennen, und letztendlich zu greifen.
Im Verlauf dieser interaktiven Exploration werden auรerdem Eigenschaften
des Objektes wie etwa sein Aussehen und seine Form ermittelt
์ธ๋ ๋ฐ ํ ํฌ ๋์ญํญ ์ ํ์ ๊ณ ๋ คํ ํ ํฌ ๊ธฐ๋ฐ์ ์์ ๊ณต๊ฐ ์ ์ด
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(์ง๋ฅํ์ตํฉ์์คํ
์ ๊ณต), 2021.8. ๋ฐ์ฌํฅ.The thesis aims to improve the control performance of the torque-based operational space controller under disturbance and torque bandwidth limitation. Torque-based robot controllers command the desired torque as an input signal to the actuator. Since the torque is at force-level, the torque-controlled robot is more compliant to external forces from the environment or people than the position-controlled robot. Therefore, it can be used effectively for the tasks involving contact such as legged locomotion or human-robot interaction. Operational space control strengthens this advantage for redundant robots due to the inherent compliance in the null space of given tasks. However, high-level torque-based controllers have not been widely used for transitional robots such as industrial manipulators due to the low performance of precise control. One of the reasons is the uncertainty or disturbance in the kinematic and dynamic properties of the robot model. It leads to the inaccurate computation of the desired torque, deteriorating the control stability and performance. To estimate and compensate the disturbance using only proprioceptive sensors, the disturbance observer has been developed using inverse dynamics. It requires the joint acceleration information, which is noisy due to the numerical error in the second-order derivative of the joint position. In this work, a contact-consistent disturbance observer for a floating-base robot is proposed. The method uses the fixed contact position of the supporting foot as the kinematic constraints to estimate the joint acceleration error. It is incorporated into the dynamics model to reduce its effect on the disturbance torque solution, by which the observer becomes less dependent on the low-pass filter design. Another reason for the low performance of precise control is torque bandwidth limitation. Torque bandwidth is determined by the relationship between the input torque commanded to the actuator and the torque actually transmitted into the link. It can be regulated by various factors such as inner torque feedback loop, actuator dynamics, and joint elasticity, which deteriorates the control stability and performance. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this work, an intuitive way to penalize low performance actuators is proposed for the operational space controller. The basic concept is to add joint torques only to high performance actuators recursively, which has the physical meaning of the joint-weighted torque solution considering each actuator performance. By penalizing the low performance actuators, the torque transmission error is reduced and the task performance is significantly improved. In addition, the joint trajectory is not required, which allows compliance in redundancy. The results of the thesis were verified by experiments using the 12-DOF biped robot DYROS-RED and the 7-DOF robot manipulator Franka Emika Panda.๋ณธ ํ์ ๋
ผ๋ฌธ์ ์ธ๋๊ณผ ํ ํฌ ๋์ญํญ ์ ํ์ด ์กด์ฌํ ๋ ํ ํฌ ๊ธฐ๋ฐ ์์
๊ณต๊ฐ ์ ์ด๊ธฐ์ ์ ์ด ์ฑ๋ฅ์ ๋์ด๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ํ ํฌ ๊ธฐ๋ฐ์ ๋ก๋ด ์ ์ด๊ธฐ๋ ๋ชฉํ ํ ํฌ๋ฅผ ์
๋ ฅ ์ ํธ๋ก์ ๊ตฌ๋๊ธฐ์ ์ ๋ฌํ๋ค. ํ ํฌ๋ ํ ๋ ๋ฒจ์ด๊ธฐ ๋๋ฌธ์, ํ ํฌ ์ ์ด ๋ก๋ด์ ์์น ์ ์ด ๋ก๋ด์ ๋นํด ์ธ๋ถ ํ๊ฒฝ์ด๋ ์ฌ๋์ผ๋ก๋ถํฐ ๊ฐํด์ง๋ ์ธ๋ ฅ์ ๋ ์ ์ฐํ๊ฒ ๋์ํ ์ ์๋ค. ๊ทธ๋ฌ๋ฏ๋ก ํ ํฌ ์ ์ด๋ ๋ณดํ์ด๋ ์ธ๊ฐ-๋ก๋ด ์ํธ์์ฉ๊ณผ ๊ฐ์ ์ ์ด์ ํฌํจํ๋ ์์
์ ์ํด ํจ๊ณผ์ ์ผ๋ก ์ฌ์ฉ๋ ์ ์๋ค. ์์
๊ณต๊ฐ ์ ์ด๋ ์ด๋ฌํ ํ ํฌ ์ ์ด์ ์ฅ์ ์ ๋ ๊ฐํ์ํฌ ์ ์๋๋ฐ, ๋ก๋ด์ด ์ฌ์ ์์ ๋๊ฐ ์์ ๋ ์์
์ ์๊ณต๊ฐ์์ ์กด์ฌํ๋ ๋ชจ์
๋ค์ด ๋ด์ฌ์ ์ผ๋ก ์ ์ฐํ๊ธฐ ๋๋ฌธ์ด๋ค. ๊ทธ๋ฌ๋ ์ด๋ฌํ ์ฅ์ ์๋ ๋ถ๊ตฌํ๊ณ ํ ํฌ ๊ธฐ๋ฐ์ ๋ก๋ด ์ ์ด๊ธฐ๋ ์ ๋ฐ ์ ์ด ์ฑ๋ฅ์ด ๋จ์ด์ง๊ธฐ ๋๋ฌธ์ ์ฐ์
์ฉ ๋ก๋ด ํ๊ณผ ๊ฐ์ ์ ํต์ ์ธ ๋ก๋ด์๋ ๋๋ฆฌ ์ฌ์ฉ๋์ง ๋ชปํ๋ค. ๊ทธ ์ด์ ์ค ํ ๊ฐ์ง๋ ๋ก๋ด ๋ชจ๋ธ์ ๊ธฐ๊ตฌํ ๋ฐ ๋์ญํ ๋ฌผ์ฑ์น์ ์กด์ฌํ๋ ์ธ๋์ด๋ค. ๋ชจ๋ธ ์ค์ฐจ๋ ๋ชฉํ ํ ํฌ๋ฅผ ๊ณ์ฐํ ๋ ์ค์ฐจ๋ฅผ ์ ๋ฐํ๋ฉฐ, ์ด๊ฒ์ด ์ ์ด ์์ ์ฑ๊ณผ ์ฑ๋ฅ์ ์ฝํ์ํค๊ฒ ๋๋ค. ์ธ๋์ ๋ด์ฌ ์ผ์๋ง์ ์ด์ฉํ์ฌ ์ถ์ ๋ฐ ๋ณด์ํ๊ธฐ ์ํด ์ญ๋์ญํ ๊ธฐ๋ฐ์ ์ธ๋ ๊ด์ธก๊ธฐ๊ฐ ๊ฐ๋ฐ๋์ด ์๋ค. ์ธ๋ ๊ด์ธก๊ธฐ๋ ์ญ๋์ญํ ๊ณ์ฐ์ ์ํด ๊ด์ ๊ฐ๊ฐ์๋ ์ ๋ณด๊ฐ ํ์ํ๋ฐ, ์ด ๊ฐ์ด ๊ด์ ์์น๋ฅผ ๋ ๋ฒ ๋ฏธ๋ถํ ๊ฐ์ด๊ธฐ ๋๋ฌธ์ ์์น์ ์ธ ์ค์ฐจ๋ก ๋
ธ์ด์ฆํด์ง๋ ๋ฌธ์ ๊ฐ ์์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ถ์ ํ ๊ธฐ์ ๋ก๋ด์ ์ํ ์ ์ด ์กฐ๊ฑด์ด ๊ณ ๋ ค๋ ์ธ๋ ๊ด์ธก๊ธฐ๊ฐ ์ ์๋์๋ค. ์ ์๋ ๋ฐฉ๋ฒ์ ๋ก๋ด์ ๊ณ ์ ๋ ์ ์ด ์ง์ ์ ๋ํ ๊ธฐ๊ตฌํ์ ์ธ ๊ตฌ์ ์กฐ๊ฑด์ ์ด์ฉํ์ฌ ๊ด์ ๊ฐ๊ฐ์๋ ์ค์ฐจ๋ฅผ ์ถ์ ํ๋ค. ์ถ์ ๋ ์ค์ฐจ๋ฅผ ๋์ญํ ๋ชจ๋ธ์ ๋ฐ์ํ์ฌ ์ธ๋ ํ ํฌ๋ฅผ ๊ณ์ฐํจ์ผ๋ก์จ ์ ์ญ ํต๊ณผ ํํฐ ์ฑ๋ฅ์ ๋ํ ์์กด๋๋ฅผ ์ค์ผ ์ ์๋ค. ํ ํฌ ๊ธฐ๋ฐ ์ ์ด์ ์ ๋ฐ ์ ์ด ์ฑ๋ฅ์ด ๋จ์ด์ง๋ ๋ ๋ค๋ฅธ ์ด์ ์ค ํ๋๋ ํ ํฌ ๋์ญํญ ์ ํ์ด๋ค. ํ ํฌ ๋์ญํญ์ ๊ตฌ๋๊ธฐ์ ์ ๋ฌ๋๋ ์
๋ ฅ ํ ํฌ์ ์ค์ ๋งํฌ์ ์ ๋ฌ๋๋ ํ ํฌ์์ ๊ด๊ณ๋ก ๊ฒฐ์ ๋๋ค. ํ ํฌ ๋์ญํญ์ ๊ตฌ๋๊ธฐ ๋ด๋ถ์ ํ ํฌ ํผ๋๋ฐฑ ๋ฃจํ, ๊ตฌ๋๊ธฐ ๋์ญํ, ๊ด์ ํ์ฑ ๋ฑ์ ์์ธ๋ค์ ์ํด ์ ํ๋ ์ ์๋๋ฐ ์ด๊ฒ์ด ์ ์ด ์์ ์ฑ ๋ฐ ์ฑ๋ฅ์ ๊ฐ์์ํจ๋ค. ์์
๊ณต๊ฐ ์ ์ด๋ ํนํ ์ด ๋ฌธ์ ์ ์ทจ์ฝํ๋ฐ, ๋์ญํญ์ด ์ ํ๋ ๊ตฌ๋๊ธฐ ํ๋๊ฐ ๊ทธ์ ์ฐ๊ด๋ ๋ชจ๋ ์์
๊ณต๊ฐ์ ์ ์ด ์ฑ๋ฅ์ ๊ฐ์์ํฌ ์ ์๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์์
๊ณต๊ฐ ์ ์ด๊ธฐ์์ ์ฑ๋ฅ์ด ๋ฎ์ ๊ตฌ๋๊ธฐ์ ์ฌ์ฉ์ ์ ํํ๊ธฐ ์ํ ์ง๊ด์ ์ธ ์ ๋ต์ด ์ ์๋์๋ค. ๊ธฐ๋ณธ ์ปจ์
์ ์์
์ ์ด๋ฅผ ์ํ ํ ํฌ ์๋ฃจ์
์ ์ฑ๋ฅ์ด ์ข์ ๊ด์ ์๋ง ์ถ๊ฐ์ ์ผ๋ก ํ ํฌ ์๋ฃจ์
์ ๋ํด๋๊ฐ๋ ๊ฒ์ผ๋ก, ์ด๊ฒ์ ๊ฐ ๊ด์ ์ ๊ฐ์ค์น๊ฐ ๊ณ ๋ ค๋ ํ ํฌ ์๋ฃจ์
์ด ๋๋ ๊ฒ์ ์๋ฏธํ๋ค. ์ฑ๋ฅ์ด ๋ฎ์ ๊ตฌ๋๊ธฐ์ ์ฌ์ฉ์ ์ ํํจ์ผ๋ก์จ ํ ํฌ ์ ๋ฌ ์ค์ฐจ๊ฐ ์ค์ด๋ค๊ณ ์์
์ฑ๋ฅ์ด ํฌ๊ฒ ํฅ์๋ ์ ์๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ค์ 12์์ ๋ ์ด์กฑ ๋ณดํ ๋ก๋ด DYROS-RED์ 7์์ ๋ ๋ก๋ด ํ Franka Emika Panda๋ฅผ ์ด์ฉํ ์คํ์ ํตํด ๊ฒ์ฆ๋์๋ค.1 INTRODUCTION 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 BACKGROUNDS 6
2.1 Operational Space Control . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Dynamics Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Fixed-Base Dynamics . . . . . . . . . . . . . . . . . . . . 9
2.2.1.1 Joint Space Formulation . . . . . . . . . . . . . 9
2.2.1.2 Operational Space Formulation . . . . . . . . . . 11
2.2.2 Floating-Base Dynamics . . . . . . . . . . . . . . . . . . . 12
2.2.2.1 Joint Space Formulation . . . . . . . . . . . . . 12
2.2.2.2 Operational Space Formulation . . . . . . . . . . 14
2.3 Position Tracking via PD Control . . . . . . . . . . . . . . . . . . 17
2.3.1 Torque Solution . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Orientation Control . . . . . . . . . . . . . . . . . . . . . 19
3 CONTACT-CONSISTENT DISTURBANCE OBSERVER FOR FLOATING-BASE ROBOTS 22
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Momentum-Based Disturbance Observer . . . . . . . . . . . . . . 24
3.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.2 External Force Estimation . . . . . . . . . . . . . . . . . . 33
3.4.3 Internal Disturbance Rejection . . . . . . . . . . . . . . . 35
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 OPERATIONAL SPACE CONTROL UNDER ACTUATOR BANDWIDTH LIMITATION 40
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.2 OSF-Based Torque Solution . . . . . . . . . . . . . . . . . 45
4.2.3 Comparison With a Typical Method . . . . . . . . . . . . 47
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4 Comparison With Other Approaches . . . . . . . . . . . . . . . . 61
4.4.1 Controller Formulation . . . . . . . . . . . . . . . . . . . . 62
4.4.1.1 The Proposed Method . . . . . . . . . . . . . . . 62
4.4.1.2 The OSF Controller . . . . . . . . . . . . . . . . 62
4.4.1.3 The OSF-Filter Controller . . . . . . . . . . . . 62
4.4.1.4 The OSF-Joint Controller . . . . . . . . . . . . . 67
4.4.1.5 The Joint Controller . . . . . . . . . . . . . . . . 68
4.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.5 Frequency Response of Joint Torque . . . . . . . . . . . . . . . . 72
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5 CONCLUSION 85
Abstract (In Korean) 100๋ฐ
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