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.