This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondarymotion to the animations with springmass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.

Figure 1: Real-time physics-based character simulation with our framework. (a) A single controller for a planar biped responds to unanticipated changes in terrain. (b) A walk controller reconstructed from motion capture data responds to a 350N,0.2s diagonal push to the torso. Physics-based simulation and control of biped locomotion is difficult because bipeds are unstable, underactuated, high-dimensional dynamical systems. We develop a simple control strategy that can be used to generate a large variety of gaits and styles in real-time, including walking in all directions (forwards, backwards, sideways, turning), running, skipping, and hopping. Controllers can be authored using a small number of parameters, or their construction can be informed by motion capture data. The controllers are applied to 2D and 3D physically-simulated character models. Their robustness is demonstrated with respect to pushes in all directions, unexpected steps and slopes, and unexpected variations in kinematic and dynamic parameters. Direct transitions between controllers are demonstrated as well as parameterized control of changes in direction and speed. Feedback-error learning is applied to learn predictive torque models, which allows for the low-gain control that typifies many natural motions as well as producing smoother simulated motion. 1

In late 2006, Nintendo released a new game controller, the Wiimote, which included a three-axis accelerometer. Since then, a large variety of novel applications for these controllers have been developed by both independent and commercial developers. We add to this growing library with three performance interfaces that allow the user to control the motion of a dynamically simulated, animated character through the motion of his or her arms, wrists, or legs. For comparison, we also implement a traditional joystick/button interface. We assess these interfaces by having users test them on a set of tracks containing turns and pits. Two of the interfaces (legs and wrists) were judged to be more immersive and were better liked than the joystick/button interface by our subjects. All three of the Wiimote interfaces provided better control than the joystick interface based on an analysis of the failures seen during the user study.

In this paper we describe an animation of a human platform diver. We simulated the motion of the diver using a dynamic model and a control system. The dynamic model is a 32 degree-of-freedom rigid body model with dynamic parameters similar to those reported in the literature for humans. The control system uses algorithms for balance, jumping, and twisting to initiate the dive, proportionalderivative servos to perform the aerial portion of the dive, and a state machine to sequence the actions throughout the dive. The motion of the simulated diver closely resembles video footage of dives performed by human athletes. The combination of dynamic simulation and a control system allowed us to animate the diver using high level commands. The control and simulation techniques presented in this paper may be useful for analysis of sports performance and for providing realistic motion for synthetic actors in computer animation and virtual environments. Keywords: Human Figure Animation, Simulatio...

Some seemingly simple behaviours such as human walking are difficult to model because of their inherent instability. This thesis proposes an approach to generating balanced 3D walking motions for physically-based computer animations by viewing the motions as a sequence of discrete cycles in state space. First, a mechanism to stabilize open loop walking motions is presented. Once this basic "balance" mechanism is in place, the underlying open loop motion can then be modified to generate variations on the basic walking gait. In addition to other interesting variations, the speed, stride rate and direction of a walk can each be controlled. These variations can be parameterized and potentially used to provide the animated character with the ability to perform autonomous motions such as following a path specified by the animator. While this work is somewhat specific to physically-based animation, some of the underlying ideas may prove useful in other disciplines such as robotics and biomech...

To realize the full potential of human simulations in interactive environments, we need controllers that have the ability to respond appropriately to unexpected events. In this paper, we create controllers for the trip recovery responses that occur during walking. Two strategies have been identified in human responses to tripping: impact from an obstacle during early swing leads to an elevating strategy, in which the swing leg is lifted over the obstacle and impact during late swing leads to a lowering strategy, in which a swing leg is positioned immediately in front of the obstacle and then the other leg is swung forward and positioned in front of the body to allow recovery from the fall. We design controllers for both strategies based on the available biomechanical literature and data captured from human subjects in the laboratory. We evaluate our controllers by comparing simulated results and actual responses obtained from a motion capture system. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation; I.6.8 [Simulation and Modeling]: Types of Simulation—Reactive responses

Our aim is to sketch the boundaries of a parallel track in the evolution of robotic forms that is radically different from any previously attempted. To do this we will first describe the motivation for doing so and then the strategy for achieving it. Along the way, it will become clear that the machines we design and build are not robots in any traditional sense. They are not machines designed to perform a set of goal oriented tasks, or work, but rather to express modes of survivalist behavior: the survival of a mobile autonomous machine in an a priori unknown and possibly hostile environment. We use no notion of conventional "intelligence " in our designs, although we suspect some strange form of that may come later. Our topic is survival oriented machines, and it turns out that intelligence in any sophisticated form is unnecessary for this concept. For such machines, if life is provisionally defined as that which moves for its own purposes, then we are dealing with living machines and how to evolve them. We call these machines biomorphs ( BIOlogical MORPHology), a form of parallel life. Introduction to Biomorphic Machines

Abstract — We propose a framework for learning biped locomotion using dynamical movement primitives based on nonlinear oscillators. In our previous work, we suggested dynamical movement primitives as a central pattern generator (CPG) to learn biped locomotion from demonstration. We introduced an adaptation algorithm for the frequency of the oscillators based on phase resetting at the instance of heel strike and entrainment between the phase oscillator and mechanical system using feedback from the environment. In this paper, we empirically explore the role of phase resetting in the proposed algorithm for robust biped locomotion. We demonstrate that phase resetting contributes to robustness against external perturbations and environmental changes by numerical simulations and experiments with a physical biped robot. I.

Abstract — We present a framework for composing motor controllers into autonomous composite reactive behaviors for bipedal robots and autonomous, physically-simulated humanoids. A key contribution of our composition framework is an explicit model of the “pre-conditions ” under which motor controllers are expected to function properly. Pre-conditions may be determined manually or learned automatically by algorithms based on Support Vector Machine (SVM) learning theory. We demonstrate controller composition and evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of regaining an upright stance after a fall. I.

Human motion plays a key role in the production of films, video games, virtual reality applications, and the control of humanoid robots. Unfortunately, it is hard to generate high quality human motion for character animation either manually or algorithmically. As a result, approaches based on motion capture data have become a central focus of character animation research in recent years. We observe three principal weaknesses in previous work using data-driven approaches for modelling human motion. First, basic balance behaviours and locomotion tasks are currently not well modelled. Second, the ability to produce high quality motion that is responsive to its environment is limited. Third, knowledge about human motor control is not well utilized. This thesis develops several techniques to generalize motion capture character animations to balance and respond. We focus on balance and locomotion tasks, with an emphasis on responding to disturbances, user interaction, and motor control integration. For this purpose, we investigate both kinematic and dynamic models. Kinematic models are intuitive and fast to construct, but have narrow generality, and thus require