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Character Physics and Animation in Full Auto David Wu Pseudo Interactive

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Introduction Physical simulation of characters is an important problem in games. In this lecture, the Framework for character simulation and animation that PI is developing will be presented.

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Introduction (cont) This framework is part of the latest incarnation of our physics engine, and is the basis for a game in development. This means that the framework needs to actually work.

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Goal The primary goal of this presentation is to describe a collection of algorithms that can be used to solve the dynamic equations of characters, incorporate animation data into the solutions and integrate the results forward in time.

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Goal The main algorithms are: The Structurally Recursive methods for inverse dynamics Implicit Euler integration Mathematical Optimization

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Goal (cont) By the end of the lecture you should have an intuitive understanding of how the structurally recursive methods work to solve for the dynamics of a character and how implicit Euler can use the results to evolve the system dynamics through time.

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Framework 1) Q 3) Residual 0) Q initial guess 2) Q,Q,Q Note: Colorful text suggested by Casey

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Framework 1) Q 3) Residual 0) Q initial guess 2) Q,Q,Q Note: Colorful text suggested by Casey

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Goal (continued) The secondary goal of this presentation is to get myself a free pass to GameTech.

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The Framework Physics in games is similar to where 3d rendering was in the early 90s. There are many different approaches to solving the problem. There is no commonly accepted standard. I.e. no Triangle.

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The Framework I predict that this framework will become the Triangle of physics in games. 1) Q 3) Residual 0) Q initial guess 2) Q,Q,Q Note: Colorful text suggested by Casey

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The Framework Versatile Robust Stable performance Simple API Explicit Control of DOF useful for Animation 1) Q 3) Residual 0) Q initial guess 2) Q,Q,Q Note: Colorful text suggested by Casey

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Disclaimer I am often wrong.

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Physical Entities All Physical entities are made up of Frames A Frame has Degrees of Freedom (DOF) and can perform spatial transformation from local space to global space. For this lecture we will only consider rigid bodies, although the algorithms that are described can support arbitrary DOF, including, for example, finite element nodes.

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Character Example Each set of arrows is a Frame All frames are related to their parent using relative coordinates Each frame is Rigid from a physics stand point (although the mesh uses skinning) Closed loops require constraints

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Character Example DOF Breakdown: Root (chest) 6 Pelvis 3 Thighs 3 (*2) Knees 1 (*2) Feet 3 (*2) Shoulders 3 (*2) Elbows 1 (*2) Wrists 3 (*2) Total: 14 Frames, 37 DOF

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Physics of Characters There are a number of ways to derive the dynamics of characters. The method that I will describe is based on the structurally recursive methods. There are a number related techniques that all use the same concepts: Featherstone's method Articulated Rigid Body Method Recursive Newton-Euler Equations Composite Rigid Body Method Etc.

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Physics of Characters An intuitive understand of how the method works is important. As such, the presentation will start from first principles and emphasize verbal descriptions. Part of the motivation behind this is the difficulty of writing equations or code in PowerPoint.

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First Principles Consider the momentum of a single rigid body at time t0: M t0 *V t0 M is a sparse 6x6 Matrix Mass Moments of Inertia V is a 6d vector 3d Velocity at Center of mass 3d Angular velocity This is often called the Spatial Velocity Vector M v w

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Some notes on the M matrix Since M is in global coordinates, it is generally dependant on the rigid bodies orientation in space. This is what gives rise to the coriolis terms If V was chosen to be at a point other than the center of mass, M would be less sparse and it would represent centrifugal effects. Using the momentum equations we can safely ignore these effects - they are handled implicitly. I.e. when a valid solution is found, derivatives of M will have contributed to the solution.

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Dynamics Conservation of momentum provides the following constraint: M t1 *V t1 = M t0 *V t0 + integral(F,t0,t1) F are external forces and torques By the definition of velocity we have: Q t1 = Q t0 + integral(V,t0,t1) Q is position and orientation These are the main equations that we need to solve to ensure that the simulation is physically accurate.

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How to solve? Everyones favorite straw man…

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Forward Euler In order to approximate integral(F,t0,t1) we could compute F at t0 and then multiply it by (t1-t0). M t1 *V t1 = M t0 *V t0 + F t0 *dt This is convenient because we know the state of the system at t0, so we can just compute forces directly. The approximation is that the forces at t0 are constant throughout the time step, which is pretty accurate if t1-t0 is very small.

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Forward Euler Another difficulty is computing M t1 We dont have the system state at t1 so computing the system inertia at that time is not trivial. If we further assumed that M t1 = M t0 we would have: M t0 *V t1 = M t0 *V t0 + F t0 *dt Multiplying everything by M t0 -1 : (V t1 -V t0 ) = M t0 -1 *F t0 *dt This can be solved explicitly, we are extrapolating acceleration. Unfortunately it is not very stable.

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Implicit Euler With Forward Euler, we replaced all Integral(X,t0,t1)s with X t0 *(t1-t0) Assuming that we can ignore the difficulties of computing quantities at future points in time, we could instead replace Integral(X,t0,t1) with X t1 *(t1-t0) This is implicit Euler, which is unconditionally stable for stable systems.

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Implicit Euler A simple example is a stiff spring F t0 F t1 V t0 V t1 There is negative feedback for conservative potentials

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Implicit Euler We can now approximate integral(F) as F t1 : M t1 *V t1 = M t0 *V t0 + F t1 *dt And we can similarly approximate integral(V,t0,t1) as V t1 *dt. Q t1 = Q t0 + V t1 *dt For convenience, we will add a pseudo acceleration equation to represent delta V from t1 to t0: V t1 = V t0 + V*dt

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Nonlinear Equations The implicit equations are difficult to solve because we need values at future points in time. Another way of saying this is that we have unknowns all over the place, and some of the relationships are nonlinear.

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Nonlinear Equations As it turns out, these equations can be transformed into a convex optimization problem. The function that we are minimizing is related to the systems kinetic and potential energy: E=(M t1 *V t1 - M t0 *V t0 - F t1 *dt)*V t1 The unknowns are the accelerations The convex constraints are the physical constraints

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Convex Optimization As it turns out, these equations can be transformed into a convex optimization problem. The function that we are minimizing is related to the systems kinetic and potential energy (the Lagrangian): E=(M t1 *V t1 - M t0 *V t0 - F t1 *dt)*V The unknowns are the accelerations (V) The convex constraints are the physical constraints

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Minimizing Energy E=(M t1 *V t1 - M t0 *V t0 - F t1 *dt)*V In the limit as |t1-t0| -> 0, minimizing this function is equivalent to minimizing the system Lagrangian. The constants are different, but the minima is at the same place and the system preserves momentum and energy.

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Interior point methods E=(M t1 *V t1 - M t0 *V t0 - F t1 *dt)*V One way to solve convex optimization problems is piecewise quadratic programming, another is the interior point method which is what we will use. The interior point methods transform the constraints into penalty functions and then minimizes unconstrained nonlinear equations.

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Penalty Methods Penalty methods are intuitive - game developers have been using them for years, even developers who dont know what they are. The spring analogy provides a metaphor and API that is easy to use and understand. You dont have to worry too much about overconstrained systems, which are very difficult to avoid in games.

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Nonlinear Equations If we know what the acceleration is, we can step the system forward in time to compute V t1, M t1. With this updated state we can compute F t1. We can then compute the Residual: R = M t1 *V t1 - M t0 *V t0 - F t1 *dt To see if the acceleration was physically correct. Ft1 Vt1 Vt0 step

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Mathematical Optimization There is a good management analogy here: Rather than trying to find a solution, we can let someone else try out solutions and just let them know how wrong they are. This is essentially the interface to mathematical optimizers i.e. Conjugate Gradient Method, Newton's method, Gauss Seidel iterations

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Minimizing Residual For a given acceleration we compute R = M t1 *V t1 - M t0 *V t0 - F t1 *dt. R is how wrong the acceleration is. - If R is 0, the solution is correct. - In practice we dont need exact 0s, if we sufficiently minimize R the answer is good enough.

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Optimizer API The mathematical optimizer will use the residual to guide a search. The search tests a series of accelerations and eventually converges on the optimal solution. Q Residual

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Hessian Newtons Method minimizes nonlinear equations by approximating them as a piecewise quadratic function. The Hessian is the matrix that represents the quadratic coefficients: q*H*q t + q t *b + c = 0

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Hessian The Hessian of these equations turns out to be: System inertia with respect to the DOF + a low pass filter due to implicit Euler integration + a filter due to constraints and penalty methods

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Hessian Intuitively the equation that the optimizer solves is similar to F = MA M is the Hessian. Constraints add infite inertia Stiff springs make the system heavier w.r.t. their direction of action

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Hessian Due to the nature of physics, if we linearalize the equation: R = M t1 *V t1 - M t0 *V t0 - F t1 *dt. about a given acceleration, the Hessian is symmetric and positive definite. This can be solved with the Conjugate gradient method

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Hessian Semi implicit Euler assumes that the Hessian is constant. This will not handle unilateral constraints, or highly nonlinear terms I.e. collisions, friction

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Truncated Newton Fully implicit Euler requires a full non linear solve, which handles a non- constant Hessian. Truncated Newton uses piecewise truncated linear solutions in conjunctions with nonlinear line searches.

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Truncated Newton If the Hessian does not change significantly fully implicit is as efficient as partially efficient. It only does extra work when required.

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Simulation In order to simulate the rigid body we: Guess at what the acceleration is, and give this to the optimizer. The optimizer will feed us a number of guesses. For each guess we: Step the system forward with the kinematic equations: V t1 = V t0 + V*dt Q t1 = Q t0 + V t1 *dt Compute F t1 Compute M t1 *V t1 Compute the residual: R=M t1 *V t1 - M t0 *V t0 - F t1 *dt Feed the residual back to the optimizer.

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Simulation In the case of unconstrained rigid bodies, the Hessian is Diagonally dominant, Conjugate gradient can solve these systems to a reasonable error in one step.

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Inverse Dynamics Computing M t1 *V t1 - M t0 *V t0 is similar to Inverse Dynamics, but in this case we are dealing with a finite change in time and velocity rather than an infinitesimal change. Inverse Dynamics computes forces from accelerations. The problems are similar enough that we can use similar techniques

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Inverse Dynamics Inverse dynamics is generally much easier than forward dynamics. A number of efficient methods for computing inverse dynamics have been developed by the robotics community. A common application is to find a set of actuator torques required to achieve a set of joint accelerations This technique can be used to efficiently determine physical quantities from animation data

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Degrees of Freedom In the case of a single unconstrained rigid body, the Spatial velocity vector is a natural choice to represent degrees of freedom. However in many cases we may want to use different degrees of freedom. Angular velocity about a specific axis (i.e. elbow joint) Linear velocity at an arbitrary point in space Time derivative of a quaternion Angular velocity of one body relative to another Etc.

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Jacobian For any set of degrees of freedom, we can derive the resulting spatial velocity and compute momentum from there. This transformation a Jacobian. dV/dq q wV Jacobian

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dV/dq A rectangular matrix of partial derivatives that relates the derivatives of generalized DOF to the velocity of Rigid bodies. q wV Jacobian

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Degrees of Freedom Rigid bodies with their spatial velocities serve as a convenient Interface for various things such as constraints, potentials, contacts, computation of momentum etc. So most systems can only worry about rigid bodies and not deal with the fact that it might be a part of a character. Attempting to interface at this level of abstraction is not recommended at social functions. q wV Jacobian

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Degrees of Freedom We can use the Jacobian to project spatial forces back into the coordinate system of the DOF. The Jacobian is often represented symbolically to exploit its sparsity and recursive relationships. I.e. for a typical character the complete Jacobian might be a 37x84 matrix tau Jacobian F

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Simulation- arbitrary DOF Q Residual w.r.t. Q V Spatial Residual (Momentum error)

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Simulation- Hessian Q V A Hessian is the System inertia projected into DOF space + a low pass filter due to Implicit Euler

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Simulation- arbitrary DOF So in order to simulate any physical mechanism we can do the following: Given the system state at t0, and an acceleration q. Compute updated positions and velocities (q,q) Compute spatial velocity (V), position and orientation of the Rigid bodies Compute the Momentum of the rigid bodies Compute external forces Compute the residual R=M t1 *V t1 - M t0 *V t0 - F t1 *dt Project the residual back into DOF space for the Mathematical Optimization system to deal with

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v w Constrained Rigid Body Consider a rigid body constrained to rotate about an axis a. It has 1 degree of freedom (q, q) The Jacobian relating q to V is the 1x6 matrix: J = { Cross(a,r), a } t V = J*q M*V = M*J*q So the residual in terms of q projected onto q is: R=(M*J*(q+q*dt) – M t0 *V t0 - F) * J t a r F

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Linked Bodies Consider the two Linked bodies, L0 and L1 L0 is the Parent of L1 L1 is pinned to L0 at the point p L1 has 3 Degrees of freedom relative to L0, it can rotate about the point. The degrees of freedom that we will use are 6 spatial DOF for L0 and 3 relative angular DOF for L1 (q1), which are in the local space of L0. M v w M v w L0 L1 F F p q1

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Linked Bodies Kinematics R0 is the vector from L0s center of mass to the pivot p R1 is the vector from p to L1s center of mass. A0 is the orientation of L0 The angular velocity of L1 is: Vl0.angular + A0*q1 The linear velocity of L1 is: Vl0.linear + cross(Vl0.angular,r0) + cross(A0*q1,r1) M v w M v w L0 L1 F F p r0 r1

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Jacobians The Jacobian for L0 is just I for its own DOF (since it is using spatial velocity) and 0 for L1s DOF. The Jacobian for L1 projects from L0s DOF and L1s DOF to the spatial velocity of L1, so it is a 9x6 matrix which computes: Vl0.angular + q1 Vl0.linear + cross(Vl0.angular,r0+r1) + cross(A0*q1,r1) Writing this expression as a matrix in PowerPoint is beyond the scope of this presentation. M v w M v w L0 L1 F1 F0 p r0 r1

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Jacobians The complete Jacobian for the system is 9x12. While it is tempting to just treat the Jacobian as a black box matrix, it is helpful to have an intuitive feel for what is happening. M v w M v w L0 L1 F1 F0 p r0 r1

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Jacobians In the case of articulated rigid bodies the block corresponding to the relationship between some q and a childs spatial velocity can be computed by: Projecting the q to a spatial vector Usually a rotation or an projection along an axis Translating the spatial velocity to the cofm of the rigid body linear = linear + cross(angular, r) angular = angular M M v w L0 L1 p r0 r1

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Jacobians Conceptually you think of L0 and L1 as one large composite rigid body. Angular velocity is constant throughout Linear velocity at a point p1 (i.e. the cofm of L1) is the linear velocity of the composite body at point p0 + cross(angular velocity, p1-p0) L0 L1 p0 p1 p1-p0 V w

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Jacobians When projecting from spatial forces back into DOF the block corresponding to the relationship between the force on a body and a parents DOF can be computed by: Translating the spatial force to pivot (or cofm) of the DOF Linear = linear angular = angular + cross(r,linear) Projecting the spatial vector onto the DOF Usually a rotation or an projection along an axis This is the Transpose of what we saw on the last slide M M L0 L1 F1 F0 p r0 r1

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Jacobians Considering L0 and L1 as one large composite rigid body, Linear Force is applied at any point results in a constant force throughout Linear Force applied to point p1 creates an addition angular torque (moment) at p0 equal to cross(p1- p0, F) L0 L1 p0 p1 p1-p0 F

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Residual Armed with the Jacobian, computing the residual is straight forward. Spatial Momentum (M*V) is computed for each body Spatial forces (F0,F1) are computed on each body as usual The residual is computed for each body: R=(Mt1*Vt1 – Mt0*Vt0 - F) The residuals are projected onto the DOF using the Jacobian. M v w M v w L0 L1 F1 F0 p r0 r1

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N Bodies This solution is readily extendable to tree structured mechanisms with an arbitrary number of degrees of freedom. I.e. characters that are not wearing straight jackets, hand cuffs or other devices that might create closed loops.

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N Bodies Direct computation of the Jacobian requires O(n 2 ) time and O(n 2 ) space. We can use a recursive technique that processes direct child-parent relationships to compute the product of the Jacobian and a vector in O(n) time. These are called the Recursive Newton Euler equations.

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N Bodies The process is a straight forward extension of what was used to solve the two body case. From Root to Leaves: Each frame uses its acceleration to update its relative velocity, position and orientation. Frames combine the effects of their relative DOF with their parents composite spatial DOF to compute their new spatial DOF.

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N Bodies Compute External Forces on all bodies From Leaves to Root, for each frame: Compute spatial momentum Compute local residual Add the composite residual of all children Project the residual onto DOF

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N Bodies When a parent passes its composite velocity down to its children it is providing the results from O(n) grand parents. When each child passes its composite residual up to its parent it is providing the results of O(n) grandchildren. This is how the problem is reduced from O(n 2 ) time to O(n) time.

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Just another Jacobian The DOF of Articulated characters can seamlessly interact with the DOF other dynamic systems We just a way to compute the Jacobian for the DOF Dof -Kinematics -Inertia -Potentials Particles Rigid Bodies Articulated Figures Finite Elements

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Just another Jacobian

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Forward Dynamics This technique can be extended to forward dynamics I.e. the Articulated rigid body method Unfortunately when you have constraints and closed loops (i.e. contacts) things rapidly increase above O(n) time. Forward dynamics can be used to precondition the system for the optimizer.

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Kinematics A convenient property of this solution is that specific DOF can be set to kinematic with very few changes: The accelerations of the kinematic DOF are not given to the mathematical optimizer, instead they are assumed to be constants. When the residual is computed for Kinematic DOF, it is not given back to the optimizer.

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Kinematics Kinematic DOF reduce the number of unknowns that the optimizer has to solve for. This improves convergence and speeds up the solution. A technique that we use to LOD simulation is to Bake certain DOF when stress is high. I.e. wrists and ankles are dynamic ¼ of the time, and kinematic with q = -q*kd the rest of the time.

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Animation Animation data can be applied to dynamic characters as follows: All DOF are set to kinematic with q derived from the animation The DOF space residuals are compared with some threshold representing the maximum force/torque that the characters muscles can apply. If the residual is <= this limit, everything is fine. If the residual is >= this limit, the limit is applied and the DOF is switched to dynamic.

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Animation This provides a very efficient mechanism to combine animations with physics. When all residuals are within limits, the costs are very similar to that of straight animation playback. I.e. there are 0 unknown DOF, which is easy to solve for.

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Animation Switching back and forth between dynamic and kinematic is perfectly legal within the context of convex optimization, provided that the actuator force applied when the DOF are dynamic are >= than the highest residual seen when they were kinematic.

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Animation Rather than removing the kinematic DOF from the optimizer, you can just set the residuals to 0 and the optimizer will not change the DOF. This is equivalent to applying a force that is exactly equal and opposite to the residual at the DOF, which is what an ideal actuator would do. The function is min(torque limit,residual) This would break a semi implicit integrator.

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Semi Implicit vs Fully Implicit When the torque hits the limit, the Hessian changes. Semi implicit integrators consider the Hessian to be constant A non-linear optimizer will find the transition in the line search.

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Limitations Finding the wrong root. A non-linear system can have multiple local minima. The worst offenders are rotational DOF with very little inertia subject to large external forces I.e. foot on ground supporting body Luckily the animation actuator increases the effective inertia of the DOF It helps to give characters strong calves. In order for this to be a problem at a 60hz simulation rate, you have to encounter angular accelerations that are ~ 3600 radians/second 2

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Limitations First order accurate For us the error is acceptable when simulating at >= 60hz. Adds damping

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Summary Lots of stuff was covered.

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Questions

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