Mixed Position and Twist Space Synthesis of 3R Chains

Mixed-position kinematic synthesis is used to not only reach a certain number of precision positions, but also impose certain instantaneous motion conditions at those positions. In the traditional approach, one end-effector twist is defined at each precision position in order to achieve better guidance of the end-effector along a desired trajectory. For one-degree-of-freedom linkages, that suffices to fully specify the trajectory locally. However, for systems with a higher number of degrees of freedom, such as robotic systems, it is possible to specify a complete higher-dimensional subspace of potential twists at particular positions. In this work, we focus on the 3R serial chain. We study the three-dimensional subspaces of twists that can be defined and set the mixed-position equations to synthesize the chain. The number and type of twist systems that a chain can generate depend on the topology of the chain; we find that the spatial 3R chain can generate seven different fully defined twist systems. Finally, examples of synthesis with several fully defined and partially defined twist spaces are presented. We show that it is possible to synthesize 3R chains for feasible subspaces of different types. This allows a complete definition of potential motions at particular positions, which could be used for the design of precise interaction with contact surfaces.Peer ReviewedPostprint (author's final draft

On the structure of natural human movement

Understanding of human motor control is central to neuroscience with strong implications in the fields of medicine, robotics and evolution. It is thus surprising that the vast majority of motor control studies have focussed on human movement in the laboratory while neglecting behaviour in natural environments. We developed an experimental paradigm to quantify human behaviour in high resolution over extended periods of time in ecologically relevant environments. This allows us to discover novel insights and contradictory evidence to well-established findings obtained in controlled laboratory conditions. Using our data, we map the statistics of natural human movement and their variability between people. The variability and complexity of the data recorded in these settings required us to develop new tools to extract meaningful information in an objective, data-driven fashion. Moving from descriptive statistics to structure, we identify stable structures of movement coordination, particularly within the arm-hand area. Combining our data with numerous published findings, we argue that current hypotheses that the brain simplifies motor control problems by dimensionality reduction are too reductionist. We propose an alternative hypothesis derived from sparse coding theory, a concept which has been successfully applied to the sensory system. To investigate this idea, we develop an algorithm for unsupervised identification of sparse structures in natural movement data. Our method outperforms state-of-the-art algorithms for accuracy and data-efficiency. Applying this method to hand data reveals a dictionary of \emph{sparse eigenmotions} (SEMs) which are well preserved across multiple subjects. These are highly efficient and invariant representation of natural movement, and suggest a potential higher-order grammatical structure or ``movement language''. Our findings make a number of testable predictions about neural coding of movement in the cortex. This has direct consequences for advancing research on dextrous prosthetics and robotics, and has profound implications for our understanding of how the brain controls our body.Open Acces

Physically based mechanical metaphors in architectural space planning

Physically based space planning is a means for automating the conceptual design process by applying the physics of motion to space plan elements. This methodology provides for a responsive design process, allowing a designer to easily make decisions whose consequences propagate throughout the design. It combines the speed of automated design methods with the flexibility of manual design methods, while adding a highly interactive quality and a sense of collaboration with the design. The primary assumption is that a digital design tool based on a physics paradigm can facilitate the architectural space planning process. The hypotheses are that Newtonian dynamics can be used 1) to define mechanical metaphors to represent the elements in an architectural space plan, 2) to compute architectural space planning solutions, and 3) to interact with architectural space plans. I show that space plan elements can be represented as physical masses, that design objectives can be represented using mechanical metaphors such as springs, repulsion fields, and screw clamps, that a layout solution can be computed by using these elements in a dynamical simulation, and that the user can interact with that solution by applying forces that are also models of the same mechanical objects. I present a prototype software application that successfully implements this approach. A subjective evaluation of this prototype reveals that it demonstrates a feasible process for producing space plans, and that it can potentially improve the design process because of the quality of the manipulation and the enhanced opportunities for design exploration it provides to the designer. I found that an important characteristic of this approach is that representation, computation, and interaction are all defined using the same paradigm. This contrasts with most approaches to automated space planning, where these three characteristics are usually defined in completely different ways. Also emerging from this work is a new cognitive theory of design titled 'dynamical design imagery,' which proposes that the elements in a designer's mental imagery during the act of design are dynamic in nature and act as a dynamical system, rather than as static images that are modified in a piecewise algorithmic manner

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Disentangling Content and Motion for Text-Based Neural Video Manipulation

Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision. Recent advances in deep generative models have led to new approaches that give promising results towards this goal. In this paper, we introduce a new method called DiCoMoGAN for manipulating videos with natural language, aiming to perform local and semantic edits on a video clip to alter the appearances of an object of interest. Our GAN architecture allows for better utilization of multiple observations by disentangling content and motion to enable controllable semantic edits. To this end, we introduce two tightly coupled networks: (i) a representation network for constructing a concise understanding of motion dynamics and temporally invariant content, and (ii) a translation network that exploits the extracted latent content representation to actuate the manipulation according to the target description. Our qualitative and quantitative evaluations demonstrate that DiCoMoGAN significantly outperforms existing frame-based methods, producing temporally coherent and semantically more meaningful results

Human to robot hand motion mapping methods: review and classification

In this article, the variety of approaches proposed in literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macro-categories the great quantity of presented methods, that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology and declared goals of the mappings. Firstly, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under six categories: direct joint, direct Cartesian, taskoriented, dimensionality reduction based, pose recognition based and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors’ point of view regarding future desirable trends are reported.This work was supported in part by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL under Grant 870133 and in part by the Spanish Government under Grant PID2020-114819GB-I00.Peer ReviewedPostprint (published version

Advances in Robot Kinematics : Proceedings of the 15th international conference on Advances in Robot Kinematics

International audienceThe motion of mechanisms, kinematics, is one of the most fundamental aspect of robot design, analysis and control but is also relevant to other scientific domains such as biome- chanics, molecular biology, . . . . The series of books on Advances in Robot Kinematics (ARK) report the latest achievement in this field. ARK has a long history as the first book was published in 1991 and since then new issues have been published every 2 years. Each book is the follow-up of a single-track symposium in which the participants exchange their results and opinions in a meeting that bring together the best of world’s researchers and scientists together with young students. Since 1992 the ARK symposia have come under the patronage of the International Federation for the Promotion of Machine Science-IFToMM.This book is the 13th in the series and is the result of peer-review process intended to select the newest and most original achievements in this field. For the first time the articles of this symposium will be published in a green open-access archive to favor free dissemination of the results. However the book will also be o↵ered as a on-demand printed book.The papers proposed in this book show that robot kinematics is an exciting domain with an immense number of research challenges that go well beyond the field of robotics.The last symposium related with this book was organized by the French National Re- search Institute in Computer Science and Control Theory (INRIA) in Grasse, France

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

Proceedings of the Workshop on Applications of Distributed System Theory to the Control of Large Space Structures

Two general themes in the control of large space structures are addressed: control theory for distributed parameter systems and distributed control for systems requiring spatially-distributed multipoint sensing and actuation. Topics include modeling and control, stabilization, and estimation and identification