Simulation of human motion data using short-horizon model-predictive control

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 52-56).Many data-driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model-predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low-gain proportional-derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.by Marco da Silva.S.M

Computational and Robotic Models of Human Postural Control

Currently, no bipedal robot exhibits fully human-like characteristics in terms of its postural control and movement. Current biped robots move more slowly than humans and are much less stable. Humans utilize a variety of sensory systems to maintain balance, primary among them being the visual, vestibular and proprioceptive systems. A key finding of human postural control experiments has been that the integration of sensory information appears to be dynamically regulated to adapt to changing environmental conditions and the available sensory information, a process referred to as "sensory re-weighting." In contrast, in robotics, the emphasis has been on controlling the location of the center of pressure based on proprioception, with little use of vestibular signals (inertial sensing) and no use of vision. Joint-level PD control with only proprioceptive feedback forms the core of robot standing balance control. More advanced schemes have been proposed but not yet implemented. The multiple sensory sources used by humans to maintain balance allow for more complex sensorimotor strategies not seen in biped robots, and arguably contribute to robust human balance function across a variety of environments and perturbations. Our goal is to replicate this robust human balance behavior in robots.In this work, we review results exploring sensory re-weighting in humans, through a series of experimental protocols, and describe implementations of sensory re-weighting in simulation and on a robot

Contribution to the synthesis of the general model of humanoid robot dynamics with a special focus on sports and training activities

У тези је дата суштинска анализа различитих врста кретања хуманоидних система. Показана је директна веза између хуманоидне роботике и биомеханике, њихов заједнички допринос развоју обе научне гране мећусобним прожимањем. Кретање хуманоидних система јесте најсложенија врста кретања, како са становишта биомеханике тако и са становишта хуманодне роботике. Да би успешно описали ову врсту кретања било је потребно анализирати различите врсте кретања, утврдити правилност и услове за одрживост ових врста кретања. Утврђено је да стабилност кретања хуманоидиних система не може бити адекватно описана стандардним тестовима стабилности, већ је потребно увести нове принципе и методе којима се може обезбедити стабилност кретања и поновљивост. Уведен је и објашњен појам динамичког баланса хуманоидног система, његова примена и методе провере. Показано је да је ЗМП универзални индикатор очувања динамичког баланса кретања хуманоидиних система у посматраном тренутку. Анализиране су различите врсте кретања и дата ситематизација на регуларна и нерегуларна кретања хуманоидних система. Објашњен је утицај ЗМП-а на одржавање динамичког баланса код регуларних и нерегуларних кретања као и методе за одређивање ситуација када може дођи до губитка динамичког баланса. Показано је да су могућа кретања хуманоидних система и у стању динамичког дисбаланса али под специфичним условима. Дат је осврт на раније предложени генерални приступ моделовању хуманоидних система и његовој примени у спортским и тренажним активностима као и пример моделовања једног одабраног кретања – скока у даљ из места. Објашњен је однос дужине скока у зависности од величине актуационих момената у појединим кључним зглобовима за дати хуманоидни модел са 20 степени слободе.The fundamental data analysis of various types of humanoid motion systems are presented in the thesis. Direct relationship between the humanoid robotics, and biomechanics, their joint contribution to the development of both scientific areas of the mutual interactions are demonstrated. The movement of humanoid systems is the most complex types of movement, both from the standpoint of biomechanics and humanoid robotics. In order to successfully describe this type of movement it is necessary to analyze different types of movements, and to determine whether the conditions for the sustainability of these types of movements. It is shown that the stability of motion of humanoidini systems can not be adequately described by standard tests of stability. It is necessary to introduce new principles and methods that can provide stability and repeatability of movements. The concept of dynamic balance of the humanoid systems is introduced and explained, together with its implementation and verification methods. It is shown that ZMP is universal indicator of dynamic balance preservation during the humanoidinih system movement at the observed moment of time. Different types of movements and systematization of regular and irregular motion of humanoid systems are analyzed and explained. ZMP influence on dynamic balance maintainance of regular and irregular movements are explained as well as methods for determining when a situation may come to a loss of dynamic balance. Possiblities of movements of humanoid systems during dynamic imbalances are shown under specific conditions. It also outlines the Previosly propose general approach of modeling of humanoid systems are underlined as wekk as its application in sports and training activities. Selected example of long jump simulation and modeling are explained an analysed. The relationships of the length of the jump depending on the moments actuated in the key joints for given humanoid model with 20 degrees of freedom are explained

Energy Shaping of Mechanical Systems via Control Lyapunov Functions with Applications to Bipedal Locomotion

This dissertation presents a method which attempts to improve the stability properties of periodic orbits in hybrid dynamical systems by shaping the energy. By taking advantage of conservation of energy and the existence of invariant level sets of a conserved quantity of energy corresponding to periodic orbits, energy shaping drives a system to a desired level set. This energy shaping method is similar to existing methods but improves upon them by utilizing control Lyapunov functions, allowing for formal results on stability. The main theoretical result, Theorem 1, states that, given an exponentially-stable limit cycle in a hybrid dynamical system, application of the presented energy shaping controller results in a closed-loop system which is exponentially stable. The method can be applied to a wide class of problems including bipedal locomotion; because the optimization problem can be formulated as a quadratic program operating on a convex set, existing methods can be used to rapidly obtain the optimal solution. As illustrated through numerical simulations, this method turns out to be useful in practice, taking an existing behavior which corresponds to a periodic orbit of a hybrid system, such as steady state locomotion, and providing an improvement in convergence properties and robustness with respect to perturbations in initial conditions without destabilizing the behavior. The method is even shown to work on complex multi-domain hybrid systems; an example is provided of bipedal locomotion for a robot with non-trivial foot contact which results in a multi-phase gait

Human-Inspired Balancing and Recovery Stepping for Humanoid Robots

Robustly maintaining balance on two legs is an important challenge for humanoid robots. The work presented in this book represents a contribution to this area. It investigates efficient methods for the decision-making from internal sensors about whether and where to step, several improvements to efficient whole-body postural balancing methods, and proposes and evaluates a novel method for efficient recovery step generation, leveraging human examples and simulation-based reinforcement learning