Speakers and Summaries

Antonio Bicchi

Professor, Italian Institute of Technology, University of Pisa, Genoa, Italy

On the Soft Synergy Model and Its Applications to Artificial Hands

There is a long history of beautiful and sophisticated artificial hands which had little or no impact on affordable and usable devices for robotics or prosthetics. In an effort to overcome such limitations, it is apparent that simplicity is at a premium, but also that ``simple'' is not necessarily ``easy''. Our work in recent years focused on trying to understand what is at the core of human upper limb functionalities, to develop a principled design approach to simplification. Not surprisingly, we found that some principles from human motor control can lead to a better design and control of artificial hands. I will present the main idea we used to enable such simplification, i.e. the notion of ``soft synergies'', which merges the concepts of motor synergies with an equilibrium-point hypothesis, and recent results in the development of the SoftHand Pro, a prosthetic derivative of the Pisa/IIT SoftHand technology.

Antonio Bicchi is Senior Scientist with the Italian Institute of Technology in Genoa and Professor of Robotics at the University of Pisa. He graduated from the University of Bologna in 1988 and was a postdoc scholar at MIT AI Lab in 1988–1991.  His main research interests are in Robotics, Haptics, and Control Systems. He is Editor-in-Chief of the IEEE Robotics and Automation Letters, and of the Springer series ``Briefs on Control, Automation and Robotics''. He has organized and chaired several international conferences, including the first WorldHaptics Conference (WHC'05), the Int. Symp. on robotics Research (ISRR'15),  and the Program Committee of the Int. Conf. Robotics and Automation (ICRA'16). He is the recipient of several awards and honors, including an Advanced Grant from the European Research Council for his research on human and robot hands.

Mehdi Benallegue

Researcher, National Institute of Advanced Industrial Science and Technology, Japan

Bipedal locomotion:

a continuous tradeoff between robust anticipation and energy-efficiency

Most of the time, humans do not watch their steps when walking. They walk without thinking. This gait is known to be very energy efficient thanks to the natural passive dynamics of the body. It also leverages the lowest load of cognitive-level motion generation and occurs more as a reflex control. How robust is that strategy? How rough can be the terrain to walk this way? Indeed, on more textured terrains, the humans move to another control, stiffer and safer but more costly.  We discuss how the ability to contantly establish a balance between anticipation and passive dynamics is the key factor to generate versatile and efficient walking motions. We show also some developped tools which aim to quantify this balance criterion and some strategies which allow humans to simplify this control.

Mehdi Benallegue received the ingénieur degree from the Institut National d'Informatique (INI), Algeria, in 2007, the M.Sc. degree from the University of Paris 7, Paris, France, in 2008 and the Ph.D. degree from Universit e de Montpellier 2, France, in 2011. He has been a postdoctoral researcher in a neurophyiology laboratory in Collège de France and in LAAS CNRS. He is currently a Research associate with the Humanoid Research Group in National Institute of Advanced Industrial Science and Technology, Japan. His research interests include estimation and control of legged locmotion, biomechanics, neuroscience and computational geometry.

Raphaël Dumas

Professor, IFFSTTAR, Lyon University, France

Multi-body optimisation: from kinematic constraints to knee contact and ligament forces

This presentation gives a quick overview on the theoretical and numerical aspects of the development of a multi-body dynamic model of the lower limb. The model includes foot, shank, patella, thigh, and pelvis segments. Anatomical kinematic constraints are introduced in order to model the ankle, tibio-femoral and patello-femoral joints with “parallel mechanisms” (e.g., sphere-on-plane contacts, isometric ligaments). A muscular geometry is also introduced. A first constrained optimisation (i.e., inverse kinematics) is performed to compute the kinematics of the model driven by skin markers. A second constrained optimisation (i.e., inverse dynamics, muscular redundancy) is performed to compute the corresponding dynamics including the musculo-tendon forces, contact forces and ligament forces.

Raphaël Dumas: Engineer and M. Sc. in Mechanics (INSA de Lyon, 1998), Ph.D. in Biomechanics 2002 (ENSAM de Paris, 2002), currently Senior Researcher at IFSTTAR – Université de Lyon. He is member of the Laboratoire de Biomécanique et Mécanique des Chocs UMR_T9406, head of the research team Modélisation du système musculo-squelettique. His research interest is in 3D multi-body modelling of the human musculoskeletal system applied to joint pathologies, postural and gait impairments. He is a member of the boards of Francophone Society of Biomechanics and Francophone Society for Movement Analysis in Child and Adult. He is a regular reviewer for journals in the field of biomechanics, Consulting Editor of Journal of Biomechanics. He is the author of 75 archive-journal full-papers, 100 conference proceedings, 4 book chapters and 2 patents.

Marc Ernst

Professor,  Ulm University, Germany

Living in a multisensory world: Integration of information across space and time

The brain receives information about the environment from all the sensory modalities, including vision, touch and audition. To efficiently interact with the environment, this information must eventually converge in the brain in order to form a reliable and accurate multimodal percept. This process is often complicated by the existence of noise at every level of signal processing, which makes the sensory information derived from the world imprecise and potentially inaccurate. There are several ways in which the nervous system may minimize the negative consequences of noise in terms of precision and accuracy. Two key strategies are to combine redundant sensory estimates and to utilize acquired knowledge about the statistical regularities of different sensory signals. In this talk, I elaborate on how these strategies may be used by the nervous system in order to obtain the best possible estimates from noisy sensory signals, such that we are able of efficiently interact with the environment. Particularly, I will focus on the learning aspects and how our perceptions are tuned to the statistical regularities of an ever-changing environment.

Marc Ernst studied physics in Heidelberg and Frankfurt/Main. In 2000 he received his Ph.D. degree from the Eberhard-Karls-University Tübingen for investigations into the human visuomotor behaviour, which he conducted at the Max Planck Institute for Biological Cybernetics. Starting in 2000, he spent almost 2 years as research associate at the University of California at Berkeley working with Prof. Martin Banks on psychophysical experiments and computational models investigating the integration of visual-haptic information. End of 2001, he returned to the MPI in Tübingen and became principle investigator of the Sensorimotor Lab in the Department of Prof. Heinrich Bülthoff. In 2007 he then became leader of the Max Planck Research Group on Human Multisensory Perception and Action. Beginning of 2011 he joined the University of Bielefeld and became full professor chairing the Cognitive Neuroscience Group. In Bielefeld he was also director of the Center for Interdisciplinary Research (ZiF) and scientific co-coordinator of the Cognitive Interaction Technology–Centre of Excellence (CITEC). In early 2016 Marc Ernst moved to Ulm University where he took over the chair for Applied Cognitive Psychology. Marc Ernst's scientific interest is in human multisensory perception, perceptual learning, sensorimotor integration, and men-machine interaction.

Ildar Farkhatdinov

Lecturer, Queen Mary University of London,

Honorary Lecturer, Imperial College London,  United-Kingdom.

Anthropomorphic verticality estimation and balance control

In this talk I would like to introduce my research results on verticality estimation and balance control in humans and robots. First, I will demonstrate with the help of control theory the importance of head stabilisation behaviour for robust verticality estimation and stable posture control. Then, I will present how these anthropomorphic control principles can be applied to verticality estimation in humanoid robots, motion planning in assistive systems and balance augmentation in human-exoskeleton interaction.

Ildar Farkhatdinov is a research associate in the Department of Bioengineering of Imperial College of London. He got his Ph.D. in Robotics from University Pierre and Marie Curie in 2013 UPMC Paris VI Sorbonne (Paris, France), M.Sc. in Mechanical Engineering from Korea University of Technology and Education in 2008 (Cheonan, South Korea), and B.Sc. with honours in Automation and Control from Moscow State University of Technology STANKIN (Moscow, Russia) in 2006. His primary research interests are in the field of human-robot/computer interaction, in particular haptics, teleoperation, human sensory motor system, as well in design and control of robotic systems. He currently works on human balance control and its implementation for lower limb exoskeletons exoskeletons.

Tamar Flash

Professor, Weizmann Institute, Tel-Aviv, Israel

Motor compositionality and timing:  combined geometrical and optimization approaches

In my talk I will discuss several recent research directions that we have taken to explore the different principles underlying the construction and control of complex human upper arm and gait movements. One important topic is motor compositionality, exploring the nature of the motor primitives underlying the construction of complex movements at different levels of the motor hierarchy.  The second topic which we focused on is motor timing, investigating what principles dictate the durations of complex sequential behaviors both at the level of the internal timing of different motion segments and the total durations of different types of movement.  Finally I will discuss the topic of motor coordination and the mapping between end-effector and joint motions both during arm and leg movements using various dimension reduction approaches.  The mathematical models we have used to study the above topics combine geometrical approaches with optimization models to derive motion invariants, optimal control principles and different conservations laws.

Tamar Flash is a professor at the Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Israel. She earned her BSc and MSc degrees in Physics from the Tel-Aviv University, Israel. She received her Ph.D. in Medical Physics from the Massachusetts Institute of Technology (1983) where she enrolled in the Harvard-MIT Division of Health Science and Technology. She continued with her postdoctoral training at MIT, at the Department of Brain and Cognitive Science and the Artificial Intelligence Laboratory (1983-1985).  In 1985 she joined the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science where she established a research group, focusing on motor control and robotics and also served as the department head (2004-2007).  She was a visiting professor at MIT, the College de France, Berkeley University and a fellow at the Radcliffe Institute for Advanced Studies, Harvard University. Her research interests include motor control, computational neuroscience and robotics.

HRP-2 14

Humanoid Robot

Francesco Lacquaniti

Professor, University of Rome Tor Vergata and IRCCS Santa Lucia, Italy

Biomechanics and Control of Human Locomotion

We walk in such an effortless and mindless manner that we tend to believe it is a simple task for our brain and neuromuscular system. However, this is not the case, as shown by the time it takes for children to learn walking and by the difficulty of rehabilitating patients with locomotor disturbances. Also roboticists are all too familiar with the difficulty of making humanoids walk stably and efficiently under a variety of conditions. I will review our work dealing with several aspects of human locomotion, from the first steps at birth till adulthood, in healthy and crippled people. I will describe kinematics and kinetics of different forms of locomotion, and describe the patterns of neuromuscular control. An evolutionary and comparative perspective of locomotor control will also be presented.

Francesco Lacquaniti received both an M.D. and a specialty in Neurology from the Medical Faculty of Turin University. After a post-doc in the Department of Physiology of the University of Minnesota in Minneapolis, he joined the Italian National Research Council in Milan where he has been Acting Director of IFCN until 1994. In 1994 he became full professor of Physiology at Cagliari University and, since 1997, he holds the same position in the Medical Faculty of the University of Rome Tor Vergata. He is the director of the Centre of Space Bio-medicine at the same University and the director of the Laboratory of Neuro-Motor Physiology at the Scientific Institute Santa Lucia Foundation (Rome). He has been elected to the Academia Europaea and received the Herlitzka Prize for his discoveries in the field of motor control.

Marc Latash

Professor, The Pennsylvania State University, USA

Controlled stability of action by abundant systems

When people move, they organize large, redundant (actually, abundant!) sets of elements (limbs, joints, digits, muscles, motor units, etc.) in a task–specific way by the central nervous system. Such organizations (synergies) ensure action stability, which is crucial given that both internal body states and external forces always vary. Action stability has to be controlled in a task-specific way. In particular, stability is reduced in a feed-forward manner (anticipatory synergy adjustment, ASA) if a person plans to perform a quick change of a salient performance variable. The importance of controlled stability for everyday movements is exemplified by studies of neurological patients who show deficits in both aspects of controlled stability: reduced stability during steady-state actions and small/delayed ASAs in preparation to a quick action. The physical approach to movement synergies has been developed using two theoretical frameworks. One of them is the idea of control with referent spatial coordinates for salient variables (referent configurations, RCs). The other is the idea of intention-specific stability of redundant systems developed as the uncontrolled manifold (UCM) hypothesis. I will describe a theory incorporating both the RC and UCM hypotheses and the idea of hierarchical control and illustrate it with results of several recent experimental studies. These studies used perturbations of ongoing movements, analysis of variance across repetitive trials, and analysis of motor equivalence to explore action stability during intentional and unintentional movements. I will also try to suggest implications of this line of thinking for robotics.

Mark Latash is a Distinguished Professor of Kinesiology and Director of the Motor Control Laboratory at the Pennsylvania State University. He received equivalents of B.S. in Physics and M.S. in Physics of Living Systems from the Moscow Institute of Physics and Technology, and a Ph.D. in Physiology from Rush University in Chicago. His research interests are focused on the control and coordination of human voluntary movements and movement disorders in neurological disorders. He is the author of “Control of Human Movement” (1993) “The Neurophysiological Basis of Movement” (1998, 2008), “Synergy” (2008), “Fundamentals of Motor Control” (2012), and “Biomechanics and Motor Control: Defining Central Concepts” (with V.M. Zatsiorsky, 2016). In addition, he edited nine books and published over 350 papers in refereed journals. Mark Latash served as the Founding Editor of the journal “Motor Control” (1996-2007) and as President of the International Society of Motor Control (2001-2005). He has served as Director of the annual Motor Control Summer School series since 2004. He is a recipient of the Bernstein Prize in motor control.

Jean-Paul Laumond

Directeur de Recherche, CNRS-LAAS, Toulouse University, France.

From Robotics to Biomechanics

Movement is a prerogative of living and robot systems. Plants and manipulator robots move to bring the world to them via self-centered movements. Animals and mobile robots navigate to explore the world. Human and humanoid actions are built from both types of movement. In this presentation, I will overview a robotics perspective of human walking. The tangent to locomotor trajectories is supported by the direction of the walker body. Thissimple seminal  idea is at the origin of models tending to explore the stereotypy of human walking. Then we will see how bottom-up approaches to pattern generators oper to steer most of humanoid robots. Recent studies on human walking synergies contradict these approaches. They open new paradigms for walking control based on top-down approaches.

Jean-Paul Laumond, IEEE Fellow, is a roboticist. He is Directeur de Recherche at LAAS-CNRS (team Gepetto) in Toulouse, France. His research is devoted to robot motion. In the 90's, he  has been the coordinator of two  European Esprit projects PROMotion (Planning RObot Motion) and MOLOG (Motion for Logistics), both dedicated to robot motion planning and control. In the early 2000's he created and managed Kineo CAM, a spin-off company from LAAS-CNRS devoted to develop and market motion planning technology. Kineo CAM was awarded the French Research Ministery prize for innovation and enterprise in 2000 and the third IEEE-IFR prize for Innovation and Entrepreneurship in Robotics and Automation in 2005. Siemens acquired Kineo CAM in 2012. In 2006, he launched the research team Gepetto dedicated to Human Motion studies along three perspectives: artificial motion for humanoid robots, virtual motion for digital actors and mannequins, and natural motions of human beings.

He teaches Robotics at Ecole Normale Supérieure in Paris. He has edited three books. He has published more than 150 papers in international journals and conferences in Robotics, Computer Science, Automatic Control and recently in Neurosciences.  He has been the 2011-2012 recipient of the Chaire Innovation technologique Liliane Bettencourt at Collège de France in Paris. His current project Actanthrope (ERC-ADG 340050) is devoted to the computational foundations of anthropomorphic action.

Yoshi Nakamura

Professor, University of Tokyo

 Neurobiomechanical Insights into Athletic Movements and Training

Can science and technology of neuro-biomechanics bring the insights into athletic movements and help their training? One approach to insights would be the mathematical analysis of the movements. Another approach is to build a mathematical model that reproduce the movements, which provide not only reliable results of analysis, but also a hypothetical mechanism of the movements. However, the analysis and modeling cannot offer by themselves a scientific method of training for recovery or improvements of the movements. It is a great challenge to establish scientific and mathematical methods of training. Study of the movements by outstanding athletes will be useful since they are more coherent, and less contaminated with unconscious disturbances. This talk will mostly on the recent developments of the mathematical analysis and modeling of the human movements, and the experiences on the study of athletic movements. A discussion on mathematical methods of training will also be opened.

Yoshihiko Nakamura is Professor at Department of Mechano-Informatics, University of Tokyo. He received Doctor of Engineering Degree from Kyoto University. Humanoid robotics, cognitive robotics, neuro musculoskeletal human modeling, biomedical systems, and their computational algorithms are his current fields of research. He is Fellow of JSME, Fellow of RSJ, Fellow of IEEE, and Fellow of WAAS. Dr. Nakamura serves as President of IFToMM (2012-2015). Dr. Nakamura is Foreign Member of Academy of Engineering Science of Serbia, and TUM Distinguished Affiliated Professor of Technische Universität München.

Francesco Nori

Researcher, Istituto Italiano di Tecnologia, Genevo, Italy

The geometric foundation of the dynamics of physical interaction and AnDy’s roadmap towards proficient human-robot collaboration

In this talk Francesco Nori will discuss his approach in modelling physical human-robot interaction. At the basis of this approach the idea is to model interaction as the coupling between two mechanical systems: the agent and the partner. While the agent has direct access to its internal variables (e.g. proprioception and applied forces), it can only indirectly infer the partner’s internal variables. Still these variables are fundamental to generate proficient collaborative motions during physical interaction tasks. In this sense modern robot have a blind spots since they are limited in observing human whole-body dynamics. The recently funded H2020-ICT project AnDy will resolve this blind spot by developing a sensor suit able to observe human whole body dynamics in real- time, by learning ergonomic and anticipatory models from the big data sets this suit generates, and by incorporating these models in on-line control to make collaboration more efficient.

Dr. Francesco Nori received his D.Ing. degree (highest honors) from the University of Padova (Italy) in 2002. He received his Ph.D. in Control and Dynamical Systems from the University of Padova (Italy) in 2005. From 2007 he joined the Istituto Italiano di Tecnologia, contributing significantly to the development of the iCub humanoid robot. His research interests are currently focused on whole-body motion control exploiting multiple (possibly compliant) contacts. With Giorgio Metta and Lorenzo Natale he is one of the key researchers involved in the iCub development, with specific focus on control and whole-body force regulation. Francesco is currently involved in two FP7- EU projects: CoDyCo as coordinator and Koroibot as principal investigator. In the past he has been investigator in ITALK, VIACTORS and Robotcub

 Michiel Van De Panne

 Professor, University of British Columbia, Canada

Learning Abstractions for Sensory-Motor Control

Michiel van de Panne's research interests are in physics-based animation and simulation, motion planning and control, computer graphics, robotics, and applications of machine learning to computer graphics. In 2002 he co-founded the ACM/Eurographics Symposium on Computer Animation, a leading forum for computer animation research. He has co-chaired EG CAS 1997, ACM/EG SCA 2002, Skigraph 2004, GI 2005, SBIM 2007, and SCA 2011, and served as Associate Editor of ACM Transactions on Graphics. He serves regularly on numerous program committees, including ACM SIGGRAPH, Eurographics, and ACM/EG SCA.

Olivier Stasse

Directeur de Recherche, LAAS-CNRS, Toulouse University, France

Next generation of high quality and robust humanoid robot

For almost fifteen years now, the humanoid HRP-2 has been a successful platform to develop and test new algorithms for motion generation and high level behaviors. An important ingredient for this success is its high reliability and the integration of mechatronics constraints simplifying control. In the past years, several high performance robots have been presented such as Atlas, Schaft, Walkman and HUBO-DRC. This new generation of robots are delivering more power by using state-of-the-art good practices for technologies such as hydraulic and electric actuators. In this presentation the pro and cons of this new generation of robots will be presented. An emphasis will be given on the key technological ingredients necessary to pursue the development of next generation controllers  on a platform as robust and reliable as HRP-2 and delivering enough power for real applications.

Emmanuel Todorov

Assitant Professor, University of Washington, USA

Physics-based optimization: A universal approach to intelligent control

Robots are meant to act as our servants, taking high-level commands and filling-in the details needed to carry out those commands in the physical world. Similarly, the sensorimotor system of the brain can be
seen as serving the high-level behavioral needs of the organism. In both cases the same question arises: what are the high-level commands, and how can the servant become autonomous enough to carry them out successfully, without additional human intervention in the case of robotics, and without need for a hidden oracle or homunculus in the
case of neuroscience. Optimal control is the natural answer to this question in both robotics and neuroscience. This talk will focus on control algorithms and applications to robotics, but will also discuss the relevance to neuroscience and biomechanics along the way.

The idea is to encode high-level commands as cost functions, which the low-level system then optimizes. Such optimization is inherently physics-based and computationally expensive. This is fine because both computers and brains have access to physics models (called forward models in neuroscience), and both computers and brains have vast computing resources. Indeed alternative control schemes that are not based on optimization have hard time finding a good use for these resources.

The optimization problem in control is made particularly challenging by the physics of contact, which have discontinuous nature and yet are the foundation of the interactions between the robot/organism and its
environment. We have recently made a lot of progress in this direction. We now have a physics simulator (MuJoCo) based on a new mathematical model of the physics of contact, making it suitable for use within an optimization loop. This has enabled neural networks to learn control policies and value functions for complex dynamical
systems, as well as trajectory optimizers to construct sequences of robot states and controls that accomplish the task. Trajectory optimization can even be done in real-time, generating novel behaviors on the fly. While a lot of the work has been done in simulation, we have already transferred some of the simulation-based controllers to real systems. The talk will illustrate rich motor behaviors on a range of simulated and real systems including getting up from the floor, walking, running, kicking, swimming, flying, riding a unicycle, as well as a number of dexterous hand manipulation tasks.

Emmanuel Todorov, Associate Professor, graduated from MIT in 1998 with a PhD in Cognitive Neuroscience. He joined the University of Washington from the Department of Cognitive Science at the University of California San Diego. Todorov’s research focus is intelligent control in biological and artificial systems. He combines experimental and computational methods to investigate how the brain controls the body in a variety of motor tasks. He also develops biologically-inspired control algorithms which aim to solve complex problems beyond the reach of traditional control theory.

Bram Vanderborght

Researcher, Vrije University, Belgium

Novel actuation principles for human-robot interaction

Prof. dr. ir. Bram Vanderborght received the degree in the study of Mechanical Engineering at the Vrije Universiteit Brussel in 2003. Since 2003 he was researcher at the VUB, supported by the Fund for Scientific Research Flanders (FWO). In May 2007 he received his PhD in Applied. The focus of his research was the use of adaptable compliance of pneumatic artificial muscles in the dynamically balanced biped Lucy. In May-June 2006 he performed research on the humanoids robot HRP-2 at the Joint Japanese/French Robotics Laboratory (JRL) in AIST, Tsukuba (Japan) in the research “Dynamically stepping over large obstacles by the humanoid robot HRP-2". He received a 3-year post-doc grant with mobility grant from the FWO. From October 2007-April 2010 he worked as post-doc researcher at the Italian Institute of Technology in Genova (Italy). Since October 2009, he is appointed as professor at the VUB. Since October 2011, he is also research director at the Universitatea Babes™-Bolyai, Department of Clinical Psychology and Psychotherapy with a project on robot assisted therapy with ASD children. He recieved an ERC grant on series-parallel elastic actuation. He is member of the Young Academy of the Royal Flemish Academy of Belgium for Science and the Arts and is the new EiC of the IEEE Robotics & Automation Magazine. His research interests includes cognitive and physical human robot interaction, robot assisted therapy, humanoids and rehabilitation robotics with core technology of using variable impedance actuators.Research Flanders (FWO). In May 2007 he received his PhD in Applied. The focus of his research was the use of adaptable compliance of pneumatic artificial muscles in the dynamically balanced biped Lucy. In May-June 2006 he performed research on the humanoids robot HRP-2 at the Joint Japanese/French Robotics Laboratory (JRL) in AIST, Tsukuba (Japan) in the research “Dynamically stepping over large obstacles by the humanoid robot HRP-2". He received a 3-year post-doc grant with mobility grant from the FWO. From October 2007-April 2010 he worked as post-doc researcher at the Italian Institute of Technology in Genova (Italy). Since October 2009, he is appointed as professor at the VUB. Since October 2011, he is also research director at the Universitatea Babes™-Bolyai, Department of Clinical Psychology and Psychotherapy with a project on robot assisted therapy with ASD children. He recieved an ERC grant on series-parallel elastic actuation. He is member of the Young Academy of the Royal Flemish Academy of Belgium for Science and the Arts and is the new EiC of the IEEE Robotics & Automation Magazine. His research interests includes cognitive and physical human robot interaction, robot assisted therapy, humanoids and rehabilitation robotics with core technology of using variable impedance actuators.

Gentiane Venture

Researcher, Tokyo University of Agriculture And Technology

Creating personalized dynamic models

In human motion science the dynamics plays an important role. It relates the movement of the human to the forces indispensable to achieve this movement. It also relates the human and its environment through interaction forces. Measuring this data is not always trivial, making subject specific model is even more complicated. In the past decade we have developed solutions for the computation of the dynamic quantities of humans, based on individual (subject specific) models inspired largely by Robotics geometric and dynamic calibration. In this presentation we will present the state of the art and our latest advances in this area and show some examples of applications to both humans and humanoid robots. With this research results we hope to contribute not only the field of robotics but also to the fields of biomechanics, ergonomics by providing adequate dynamic models of beings.

Bruno Watier

Assistant Professor, LAAS-CNRS, Toulouse University, France

From Biomechanics to Robotics

In spite of the apparent simplicity of a skilled movement, the organization of the underlying neuro-musculo-skeletal system remains unknown. A reason is the redundancy of the motor system: a given movement can be realized by different muscle and joint activity patterns, and the same underlying activity may give rise to several movements. Some theories, such as optimal command or motor primitives, provide tentative solutions to this conundrum, but none satisfies all empirical findings, which largely pertain to very basic movements, such as grasping or pointing. Studying full body motion, becomes essential to determine invariants of human movement. In this talk we will present examples of full body motion analysis and how it can be used to generate anthropomorphic movement.