Compliant control of Uni/ Multi- robotic arms with dynamical systems

Accomplishment of many interactive tasks hinges on the compliance of humans. Humans demonstrate an impressive capability of complying their behavior and more particularly their motions with the environment in everyday life. In humans, compliance emerges from different facets. For example, many daily activities involve reaching for grabbing tasks, where compliance appears in a form of coordination. Humans comply their handsâ motions with each other and with that of the object not only to establish a stable contact and to control the impact force but also to overcome sensorimotor imprecisions. Even though compliance has been studied from different aspects in humans, it is primarily related to impedance control in robotics. In this thesis, we leverage the properties of autonomous dynamical systems (DS) for immediate re-planning and introduce active complaint motion generators for controlling robots in three different scenarios, where compliance does not necessarily mean impedance and hence it is not directly related to control in the force/velocity domain. In the first part of the thesis, we propose an active compliant strategy for catching objects in flight, which is less sensitive to the timely control of the interception. The soft catching strategy consists in having the robot following the object for a short period of time. This leaves more time for the fingers to close on the object at the interception and offers more robustness than a âhardâ catching method in which the hand waits for the object at the chosen interception point. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction. Stability and convergence of the approach are assessed through Lyapunov stability theory. In the second part, we propose a unified compliant control architecture for coordinately reaching for grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and of the system, each arm complies not only with the objectâs motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, we propose a unified dynamical system that endows the multi-arm system with both synchronous and asynchronous behaviors and with the capability of smoothly transitioning between the two modes. At the joint space level, the compliance between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under self-collision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in real-time. In the last part, we propose a compliant dynamical system for stably transitioning from free motions to contacts. In this part, by modulating the robot's velocity in three regions, we show theoretically and empirically that the robot can (I) stably touch the contact surface (II) at a desired location, and (III) leave the surface or stop on the surface at a desired point.

Seyed Sina Mirrazavi Salehian

EPFL2018

Control and Learning of Compliant Manipulation Skills

Klas Jonas Alfred Kronander

Humans demonstrate an impressive capability to manipulate fragile objects without damaging them, graciously controlling the force and position of hands or tools. Traditionally, robotics has favored position control over force control to produce fast, accurate and repeatable motion. For extending the applicability of robotic manipulators outside the strictly controlled environments of industrial work cells, position control is inadequate. Tasks that involve contact with objects whose positions are not known with perfect certainty require a controller that regulates the relationship between positional deviations and forces on the robot. This problem is formalized in the impedance control framework, which focuses the robot control problem on the interaction between the robot and its environment. By adjusting the impedance parameters, the behavior of the robot can be adapted to the need of the task. However, it is often difficult to specify formally how the impedance should vary for best performance. Furthermore, fast it can be shown that careless variation of the impedance can lead to unstable regulation or tracking even in free motion. In the first part of the thesis, the problem of how to define a varying impedance for a task is addressed. A haptic human-robot interface that allows a human supervisor to teach impedance variations by physically interacting with the robot during task execution is introduced. It is shown that the interface can be used to enhance the performance in several manipulation tasks. Then, the problem of stable control with varying impedance is addressed. Along with a theoretical discussion on this topic, a sufficient condition for stable varying stiffness and damping is provided. In the second part of the thesis, we explore more complex manipulation scenarios via online generation of the robot trajectory. This is done along two axes 1) learning how to react to contact forces in insertion tasks which are crucial for assembly operations and 2) autonomous Dynamical Systems (DS) for motion representation with the capability to encode a family of trajectories rather than a fixed, time-dependent reference. A novel framework for task representation using DS is introduced, termed Locally Modulated Dynamical Systems (LMDS). LMDS differs from existing DS estimation algorithms in that it supports non-parametric and incremental learning all the while guaranteeing that the resulting DS is globally stable at an attractor point. To combine the advantages of DS motion generation with impedance control, a novel controller for tasks described by first order DS is proposed. The controller is passive, and has the properties of an impedance controller with the added flexibility of a DS motion representation instead of a time-indexed trajectory.

EPFL2015

Multi-contact tactile exploration and interaction with unknown objects

Nicolas Sommer

Humans rely on the sense of touch in almost every aspect of daily life, whether to tie shoelaces, place fingertips on a computer keyboard or find keys inside a bag. With robots moving into human-centered environment, tactile exploration becomes more and more important as vision may be occluded easily by obstacles or fail because of different illumination conditions. Traditional approaches mostly rely on position control for manipulating objects and are adapted to single grippers and known objects. New sensors make it possible to extend the control to tackle problems unsolved before: handling unknown objects and discovering local features on their surface. This thesis tackles the problem of controlling a robot which makes multiple contacts with an unknown environment. Generating and keeping multiple contacts points on different parts of the robot fingers during exploration is an essential feature that distinguishes our work from other haptic exploration work in the literature, where contacts are usually limited to one or more fingertips. In the first part of this thesis, we address the problem of exploring partially known surfaces and objects for modeling and identification. In multiple scenarios, control and exploration strategies are developed to compliantly follow the surface or contour of a surface with robotic fingers. Whereas the methods developed in the first part of this thesis perform well on objects with limited size and variation in shape, the second part of the thesis is devoted to the development of a controller that maximizes contact with unknown surfaces of any shape and size. Maximizing contact allows to gather information more rapidly and also to create stable grasps. To this end, we develop an algorithm based on the task-space formulation to quickly handle the control in torque of an actively compliant robot while keeping constraints, particularly on contact forces. We also develop a strategy to maximize the surface in contact, given only the current state of contact, i.e. without prior information on the object or surface. In the third part of the thesis, an additional application of the developed hand controller is explored. The problem of autonomous grasping using only tactile data is tackled. The arm motion is generated according to search and grasping strategies implemented with Dynamical Systems (DS). We extend existing approaches to locally modulate dynamical systems (DS) to enable sensing-based modulation, so as to change the dynamics of motion depending on task progress. This allows to generate fast and autonomous object localization and grasping in one flexible framework. We also apply this algorithm to teach a robot how to react to collisions in order to navigate between obstacles while reaching.

EPFL2017

Multi-contact tactile exploration and interaction with unknown objects

Nicolas Sommer

EPFL2017

Control and Learning of Compliant Manipulation Skills

Klas Jonas Alfred Kronander

EPFL2015

Compliant control of Uni/ Multi- robotic arms with dynamical systems

Seyed Sina Mirrazavi Salehian

EPFL2018