Modeling, analysis and control of robot-object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives

International audienceSo-called robot-object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an "object" and a "robot". Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot-object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article

On the discretisation of actuation in locomotion: Impulse- and shape-based modelling for hopping robots

In an age where computers challenge the smartest human beings in cognitive tasks, the conspicuous discrepancy between robot and animal locomotion appears paradoxical. While animals can move around autonomously in complex environments, today’s robots struggle to independently operate in such surroundings. There are many reasons for robots’ inferior performance, but arguably the most important one is our missing understanding of complexity. This thesis introduces the notion of discrete actuation for the study of locomotion in robots and animals. The actuation of a system with discrete actuation is restricted to be applied at a finite number of instants in time and is impulsive. We find that, despite their simplicity, such systems can predict various experimental observations and inspire novel technologies for robot design and control. We further find that, through the study of discrete actuation, causal relationships between actuation and resulting behaviour are revealed and become quantifiable, which relates the findings presented in this thesis to the broader concepts of complexity, self-organisation, and self-stability. We present four case studies in Chapters 3-6 which demonstrate how the concept of discrete actuation can be employed to understand the physics of locomotion and to facilitate novel robot technologies. We first introduce the impulsive eccentric wheel model which is a discretely actuated system for the study of hopping locomotion. We find that the model predicts robot hopping trajectories and animal related hopping characteristics by reducing the dynamics of hopping–usually described by hybrid differential equations–to analytic maps. The reduction of complexity of the model equations reveals the underlying physics of the locomotion process, and we identify the importance of robot shape and mass distribution for the locomotion performance. As a concrete application of the model, we compare the energetics of hopping and rolling locomotion in environments with obstacles and find when it is better to hop than to roll, based on the fundamental physical principles we discover in the model analysis. The theoretical insights of this modelling approach enable new actuation techniques and design for robots which we display in Robbit; a robot that uses strictly convex foot shapes and rotational impulses to induce hopping locomotion. We show that such systems outperform hopping with non-strictly convex shapes in terms of energy effective and robust locomotion. A system with discrete actuation motivates the exploitation of shape and the environment to improve locomotion dynamics, which reveals advantageous effect of inelastic impacts between the robot foot and the environment. We support this idea with experimental results from the robot CaneBot which can change its foot shape to induce timed impacts with the environment. Even though inelastic impacts are commonly considered detrimental for locomotion dynamics, we show that their appropriate control improves the locomotion speed considerably. The findings presented in this thesis show that discrete actuation for locomotion inspires novel ways to appreciate locomotion dynamics and facilitates unique control and design technologies for robots. Furthermore, discrete actuation emphasises the definition of causality in complex systems which we believe will bring robots closer to the locomotion behaviour of animals, enabling more agile and energy effective robots

Pedagogy in performance: An investigation into decision training as a cognitive approach to circus training

This research project represents the first formal research conducted into the potential application of Decision Training in an elite circus arts school environment. The research examines the effects of the introduction of Decision Training—a training model developed for sports applications—into the elite circus arts training program at the National Circus School (NCS), a key circus arts school in one of the world’s most vital circus domains, Montreal, Quebec, Canada. Decision Training, a cognitive-based training model, has been shown through extensive sports-based research to support the development of decision-making ability and self-regulatory learning behaviour, both of which are fundamental for the long-term retention and application of physical skills. A key research aim was to investigate whether Decision Training had the potential to enhance existing teaching practice at the NCS. This research investigates how this cognitive training model—developed for use in the world of competitive sports—functions in a performing arts context in which not only physical and technical skills are trained, but also elements connected with performance, such as aesthetic expression and the creation and development of new performance material. A qualitative action research methodology was employed, consisting of three reflection–action cycles with three case studies of student–teacher pairings. Data collection took place over an extended training period at the NCS from November 2011 to April 2012. Observation, interviews with teachers and students, and group discussions were used to collect data and to provide the impetus for the Decision Training interventions for the three action research cycles. This qualitative study reveals how teachers implemented the three-step Decision Training model and how students responded to these teaching interventions. This was done through an action research process investigating the lived experiences of the participants involved in each case study. The research findings indicate that incorporating a cognitive training method such as Decision Training into circus pedagogy has the potential benefit of giving students the means of acquiring important skills such as effective decision making in performance situations, and self-regulatory behaviour such as the ability to effectively self-assess their performance. Teachers have the potential to benefit by not having to be the sole providers of feedback or motivation, allowing the rapport between student and teacher to become collaborative and creative. The research findings show that the effectiveness of the Decision Training interventions was influenced by the different learning and teaching backgrounds and styles of the student–teacher pairings, and the different ways in which the teachers integrated Decision Training into their existing teaching practices. The research findings led to the proposal of an “integrated” pedagogical approach based on a combination of Decision Training and direct teaching. This “integrated” pedagogy would enable a teacher to use the cognitivist, student-centred learning approach of Decision Training to develop self-regulation and effective decision making in students, but switch to aspects of direct teaching at appropriate times: for instance, when a student needs to be directly aware of safety issues or has little foundational knowledge in a circus discipline; in the lead-up to a performance showing; or during the period in which a student is adjusting to the new cognitivist learning and teaching environment. Recommendations are made for the gradual phasing in of Decision Training into the main training program at the NCS, and implications for future research are discussed

Imitation Learning of Motion Coordination in Robots:a Dynamical System Approach

The ease with which humans coordinate all their limbs is fascinating. Such a simplicity is the result of a complex process of motor coordination, i.e. the ability to resolve the biomechanical redundancy in an efficient and repeatable manner. Coordination enables a wide variety of everyday human activities from filling in a glass with water to pair figure skating. Therefore, it is highly desirable to endow robots with similar skills. Despite the apparent diversity of coordinated motions, all of them share a crucial similarity: these motions are dictated by underlying constraints. The constraints shape the formation of the coordination patterns between the different degrees of freedom. Coordination constraints may take a spatio-temporal form; for instance, during bimanual object reaching or while catching a ball on the fly. They also may relate to the dynamics of the task; for instance, when one applies a specific force profile to carry a load. In this thesis, we develop a framework for teaching coordination skills to robots. Coordination may take different forms, here, we focus on teaching a robot intra-limb and bimanual coordination, as well as coordination with a human during physical collaborative tasks. We use tools from well-established domains of Bayesian semiparametric learning (Gaussian Mixture Models and Regression, Hidden Markov Models), nonlinear dynamics, and adaptive control. We take a biologically inspired approach to robot control. Specifically, we adopt an imitation learning perspective to skill transfer, that offers a seamless and intuitive way of capturing the constraints contained in natural human movements. As the robot is taught from motion data provided by a human teacher, we exploit evidence from human motor control of the temporal evolution of human motions that may be described by dynamical systems. Throughout this thesis, we demonstrate that the dynamical system view on movement formation facilitates coordination control in robots. We explain how our framework for teaching coordination to a robot is built up, starting from intra-limb coordination and control, moving to bimanual coordination, and finally to physical interaction with a human. The dissertation opens with the discussion of learning discrete task-level coordination patterns, such as spatio-temporal constraints emerging between the two arms in bimanual manipulation tasks. The encoding of bimanual constraints occurs at the task level and proceeds through a discretization of the task as sequences of bimanual constraints. Once the constraints are learned, the robot utilizes them to couple the two dynamical systems that generate kinematic trajectories for the hands. Explicit coupling of the dynamical systems ensures accurate reproduction of the learned constraints, and proves to be crucial for successful accomplishment of the task. In the second part of this thesis, we consider learning one-arm control policies. We present an approach to extracting non-linear autonomous dynamical systems from kinematic data of arbitrary point-to-point motions. The proposed method aims to tackle the fundamental questions of learning robot coordination: (i) how to infer a motion representation that captures a multivariate coordination pattern between degrees of freedom and that generalizes this pattern to unseen contexts; (ii) whether the policy learned directly from demonstrations can provide robustness against spatial and temporal perturbations. Finally, we demonstrate that the developed dynamical system approach to coordination may go beyond kinematic motion learning. We consider physical interactions between a robot and a human in situations where they jointly perform manipulation tasks; in particular, the problem of collaborative carrying and positioning of a load. We extend the approach proposed in the second part of this thesis to incorporate haptic information into the learning process. As a result, the robot adapts its kinematic motion plan according to human intentions expressed through the haptic signals. Even after the robot has learned the task model, the human still remains a complex contact environment. To ensure robustness of the robot behavior in the face of the variability inherent to human movements, we wrap the learned task model in an adaptive impedance controller with automatic gain tuning. The techniques, developed in this thesis, have been applied to enable learning of unimanual and bimanual manipulation tasks on the robotics platforms HOAP-3, KATANA, and i-Cub, as well as to endow a pair of simulated robots with the ability to perform a manipulation task in the physical collaboration

Data-Driven Methods to Build Robust Legged Robots

For robots to ever achieve signicant autonomy, they need to be able to mitigate performance loss due to uncertainty, typically from a novel environment or morphological variation of their bodies. Legged robots, with their complex dynamics, are particularly challenging to control with principled theory. Hybrid events, uncertainty, and high dimension are all confounding factors for direct analysis of models. On the other hand, direct data-driven methods have proven to be equally dicult to employ. The high dimension and mechanical complexity of legged robots have proven challenging for hardware-in-the-loop strategies to exploit without signicant eort by human operators. We advocate that we can exploit both perspectives by capitalizing on qualitative features of mathematical models applicable to legged robots, and use that knowledge to strongly inform data-driven methods. We show that the existence of these simple structures can greatly facilitate robust design of legged robots from a data-driven perspective. We begin by demonstrating that the factorial complexity of hybrid models can be elegantly resolved with computationally tractable algorithms, and establish that a novel form of distributed control is predicted. We then continue by demonstrating that a relaxed version of the famous templates and anchors hypothesis can be used to encode performance objectives in a highly redundant way, allowing robots that have suffered damage to autonomously compensate. We conclude with a deadbeat stabilization result that is quite general, and can be determined without equations of motion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155053/1/gcouncil_1.pd

Our Mythical Hope

Classical Antiquity is a particularly important field in terms of “Hope studies” […]. For centuries, the ancient tradition, and classical mythology in particular, has been a common reference point for whole hosts of creators of culture, across many parts of the world, and with the new media and globalization only increasing its impact. Thus, in our research at this stage, we have decided to study how the authors of literary and audiovisual texts for youth make use of the ancient myths to support their young protagonists (and readers or viewers) in crucial moments of their existence, on their road into adulthood, and in those dark hours when it seems that life is about to shatter and fade away. However, if Hope is summoned in time, the crisis can be overcome and the protagonist grows stronger, with a powerful uplifting message for the public. […] Owing to this, we get a chance to remain true to our ideas, to keep faith in our dreams, and, when the decisive moment comes, to choose not hatred but love, not darkness but light. Katarzyna Marciniak, University of Warsaw, From the introductory chapte