A Grasp-based Motion Planning Algorithm for Intelligent

Character Animation

Maciej Kalisiakmac@dgp.toronto.edu

Introduction

• human character animation• constrained environments• example problem• related research areas

Animation Techniques

• many methods:– motion capture– specific gait models– handcrafted controllers– spacetime constraints– etc.

• cannot solve our problem

Randomized Path Planning (RPP)

• freespace motion planning• piano mover’s problem• example RPP solution

Combined Approach

• borrow ideas from animation and RPP• starting point: RPP• need to add:

– knowledge of human gaits– notion of comfort– moving while in contact with environment

Simplest “Planner”

• character’s state• repeated perturbations,

i.e., Brownian motion• perturbations move COM• inefficient

Potential-guided Planner

• P(q) = COM’s shortest distance to goal

• solve using gradient descent• analytic gradient computation

not feasible• repeatedly sample q’s

neighbourhood and choose perturbations that result in largest drop in P(q)

Local Minima

• gradient descent stops at any minimum• use “random walks” to escape

– Brownian motion of predetermined duration• use “backtracking” if minimum too deep

– revert to a previous point in solution,followed by a random walk

Deep Minimum Example

Smoothing

• solution embodies complete history of search process

• also, very noisy• a trajectory filter post-process is applied

– removes extraneous motion segments– makes remaining motion fluid

Our Extensions

• grasp points• grasp constraint• comfort heuristic system• gait finite state machine• adapted gradient descent, random walk,

smoothing filters

Grasp Points

• represent potential points of contact

• reduces the grasp search space

• “grasp”: attachment of limb to grasp point

• three types

Grasp Constraint

• some number and type of grasps must always be in effect• the number and type of grasps dictated by GFSM• rest of planner must preserve existing grasps

(gradient descents, random walks, smoothing)

The Gait FSM

• provides distinct behaviours• states represent gaits• edges represent transitions • each edge has associated

preconditions and effects• GFSM consulted after every

step of the gradient descent

Heuristic System• each heuristic measures some quality of q• D(q): overall discomfort, a potential field• assuming a comfortable position consists of

using gradient descent through D(q)

Complete System

Results

Future Work

• 3D• grasp surfaces• arbitrary, non-human skeletons• complex grasping• motion speed control• learning

Contributions

• human character animation algorithmfor constrained environments– grasp point discretization of environment– grasp constraint– comfort modeling using heuristics– gait FSM– adapted RPP algorithms to grasp constraint

~FIN~MPEG movies of results available at:

http://www.dgp.toronto.edu/~mac/thesis

Appendix

(extra slides that might prove useful in answering questions)

Character Structure

bitmap and distance map

Alternate gradient descent view

Motion without Heuristics

Smoothing Algorithm

Need for Limb Smoothing

Limb Smoothing Solution

Implemented GFSM

Implemented Heuristics