Michiel van de Panne

Composable controllers for physics-based character animation

An ambitious goal in the area of physics-based computer animation is the creation of virtual actors that autonomously synthesize realistic human motions and possess a broad repertoire of lifelike motor skills. To this end, the control of dynamic, anthropomorphic figures subject to gravity and contact forces remains a difficult open problem. We propose a framework for composing controllers in order to enhance the motor abilities of such figures. A key contribution of our composition framework is an explicit model of the “pre-conditions” under which motor controllers are expected to function properly. We demonstrate controller composition with pre-conditions determined not only manually, but also automatically based on Support Vector Machine (SVM) learning theory. We 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 standing up after a fall. We furthermore demonstrate these basic controllers working in conjunction with more dynamic motor skills within a prototype virtual stunt-person. Our composition framework promises to enable the community of physics-based animation practitioners to easily exchange motor controllers and integrate them into dynamic characters.

SIMBICON: simple biped locomotion control

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.

Sensor-actuator networks

Sensor-actuator networks (SANs) are a new approach for the physically-based animation of objects. The user supplies the configuration of a mechanical system that has been augmented with simple sensors and actuators. It is then possible to automatically discover many possible modes of locomotion for the given object. The SANs providing the control for these modes of locomotion are simple in structure and produce robust control. A SAN consists of a small non-linear network of weighted connections between sensors and actuators. A stochastic procedure for finding and then improving suitable SANs is given. Ten different creatures controlled by this method are presented. CR Categorias: G.3 [Probabuity and Statistics]: Probabilistic Algorithms; 1.2.6 [Artificial Intelligence]: Learning, Robotics; 1.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism animation; 1.6.3 [Simulation and Modeling] Applications.

Motion doodles: an interface for sketching character motion

In this paper we present a novel system for sketching the motion of a character. The process begins by sketching a character to be animated. An animated motion is then created for the character by drawing a continuous sequence of lines, arcs, and loops. These are parsed and mapped to a parameterized set of output motions that further reflect the location and timing of the input sketch. The current system supports a repertoire of 18 different types of motions in 2D and a subset of these in 3D. The system is unique in its use of a cursive motion specification, its ability to allow for fast experimentation, and its ease of use for non-experts.

Limit cycle control and its application to the animation of balancing and walking

Seemingly simple behaviors such as human walking are difficult to model because of their inherent instability. Kinematic animation techniques can freely ignore such intrinsically dynamic problems, but they therefore also miss modeling important motion characteristics. On the other hand, the effect of balancing can emerge in a physically-based animation, but it requires computing delicate control strategies. We propose an alternative method that adds closedloop feedback to open-loop periodic motions. We then apply our technique to create robust walking gaits for a fully-dynamic 19 degree-of-freedom human model. Important global characteristics such as direction, speed and stride rate can be controlled by changing the open-loop behavior alone or through simple control parameters, while continuing to employ the same local stabilization technique. Among other features, our dynamic “human” walking character is thus able to follow desired paths specified by the animator.

Simplifying Flexible Isosurfaces Using Local Geometric Measures

The contour tree, an abstraction of a scalar field that encodes the nesting relationships of isosurfaces, can be used to accelerate isosurface extraction, to identify important isovalues for volume-rendering transfer functions, and to guide exploratory visualization through a flexible isosurface interface. Many real-world data sets produce unmanageably large contour trees which require meaningful simplification. We define local geometric measures for individual contours, such as surface area and contained volume, and provide an algorithm to compute these measures in a contour tree. We then use these geometric measures to simplify the contour trees, suppressing minor topological features of the data. We combine this with a flexible isosurface interface to allow users to explore individual contours of a dataset interactively.

Generalized biped walking control

We present a control strategy for physically-simulated walking motions that generalizes well across gait parameters, motion styles, character proportions, and a variety of skills. The control is realtime, requires no character-specific or motion-specific tuning, is robust to disturbances, and is simple to compute. The method works by integrating tracking, using proportional-derivative control; foot placement, using an inverted pendulum model; and adjustments for gravity and velocity errors, using Jacobian transpose control. High-level gait parameters allow for forwards-and-backwards walking, various walking speeds, turns, walk-to-stop, idling, and stop-to-walk behaviors. Character proportions and motion styles can be authored interactively, with edits resulting in the instant realization of a suitable controller. The control is further shown to generalize across a variety of walking-related skills, including picking up objects placed at any height, lifting and walking with heavy crates, pushing and pulling crates, stepping over obstacles, ducking under obstacles, and climbing steps.

Interactive control for physically-based animation

We propose the use of interactive, user-in-the-loop techniques for controlling physically-based animated characters. With a suitably designed interface, the continuous and discrete input actions afforded by a standard mouse and keyboard allow for the creation of a broad range of motions. We apply our techniques to interactively control planar dynamic simulations of a bounding cat, a gymnastic desk lamp, and a human character capable of walking, running, climbing, and various gymnastic behaviors. The interactive control techniques allows a performers intuition and knowledge about motion planning to be readily exploited. Video games are the current target application of this work.

Locomotion skills for simulated quadrupeds

We develop an integrated set of gaits and skills for a physics-based simulation of a quadruped. The motion repertoire for our simulated dog includes walk, trot, pace, canter, transverse gallop, rotary gallop, leaps capable of jumping on-and-off platforms and over obstacles, sitting, lying down, standing up, and getting up from a fall. The controllers use a representation based on gait graphs, a dual leg frame model, a flexible spine model, and the extensive use of internal virtual forces applied via the Jacobian transpose. Optimizations are applied to these control abstractions in order to achieve robust gaits and leaps with desired motion styles. The resulting gaits are evaluated for robustness with respect to push disturbances and the traversal of variable terrain. The simulated motions are also compared to motion data captured from a filmed dog.

Flexible muscle-based locomotion for bipedal creatures

We present a muscle-based control method for simulated bipeds in which both the muscle routing and control parameters are optimized. This yields a generic locomotion control method that supports a variety of bipedal creatures. All actuation forces are the result of 3D simulated muscles, and a model of neural delay is included for all feedback paths. As a result, our controllers generate torque patterns that incorporate biomechanical constraints. The synthesized controllers find different gaits based on target speed, can cope with uneven terrain and external perturbations, and can steer to target directions.