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Behavior Planning for Character Animation. Manfred Lau and James Kuffner Carnegie Mellon University. Problem. Key ideas. Motions abstracted as high-level behaviors and organized into a finite state machine (FSM). (in contrast to connections of individual poses)

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behavior planning for character animation

Behavior Planning for Character Animation

Manfred Lau and James Kuffner

Carnegie Mellon University

key ideas
Key ideas

Motions abstracted as high-level behaviors and organized into a finite state machine (FSM).

(in contrast to connections of individual poses)

Build search tree of behavior states and perform global planning in both space and time.

(in contrast to local policies)

overview
Overview

Environment

FSM

overview5
Overview

Behavior Planner

Environment

FSM

overview6
Overview

Behavior Planner

Solution Path(Sequence of Behaviors)

Environment

FSM

Animation

overview7
Overview

Dynamic Environments

Terrain with small slopes

overview8
Overview

Different behavior preferences

Variety of characters

related work
Related Work

Motion Planning

Kuffner 98Shiller et al. 01Bayazit et al. 02Choi et al. 03Pettre et al. 03Koga et al. 94Kalisiak and van de Panne 01Yamane et al. 04

GlobalNavigation

Choi et al. 03

Manipulation andwhole-body motions

related work10
Related Work

Re-playing original motion capture data

Arikan and Forsyth 02Kovar et al. 02Lee et al. 02Pullen and Bregler 02Gleicher et al. 03

Move Trees / Steering Approaches

Brogan and Hodgins 97Menache 99Reynolds 99Mizuguchi et al. 01

Kovar et al. 02

Reynolds 99

our approach
Our Approach

Manually-Constructed Behavior FSM

+ Scalability

+ Search Efficiency

+ Memory Usage

+ Intuitive Structure

– Requires segmented motion data

– Requires FSM with appropriate transitions

environment representation
Environment Representation

2D Heightfield

Obstacle Growth in Robot Path PlanningUdupa 77Lozano-Pérez and Wesley 83

behavior planner a search
Behavior Planner – A* search

initialize Tree and Queue

while Queue is not empty

remove sbest

if Goal reached

return sbest

if appropriate

expand sbest

end

return no possible solution

position orientation time cost

state dependent transitions
State-dependent Transitions

if appropriate

expand sbest

Retrieve (from FSM) the states that sbest can transition to

Updates position, orientation, cost, time

Collision checking

motion generation blending
Motion Generation / Blending

Sequence of behaviors  converted to actual motion

Blending at frames near transition points

Linearly interpolate root positions

Smooth-in, smooth-out slerp interpolation for joint rotations

environment dependent transitions
Environment-dependent Transitions

Transition regions near obstacles

(computed automatically fromenvironment geometry)

Action must completely traverse corresponding obstacle

(pass underneath, cross over, etc.)

dynamic obstacles
Dynamic Obstacles

State and Time-dependent Transitions

Movement of dynamic obstacles needs to be predictable

envt E(time)

planning for multiple characters
Planning for Multiple Characters

Centralized PlanningPlan jointly for all characters using all possible combinations of behaviors

+ returns globally optimal solutions

– search time exponential in # of characters and behaviors

Prioritized PlanningPlan for each character in turn according to priority

– not as general

– returns globally non-optimal solutions

+ linear time in the # of characters

optimality speed tradeoff
Optimality / Speed Tradeoff

A* search truncated A* inflated A*

2241 nodes 1977 nodes 1421 nodes

faster than A* search by 6% 16%

anytime version of algorithm
“Anytime” Version of Algorithm

initialize Tree and Queue

while (!Q.empty() and t < Tmax)

remove sbest

if Goal reached

return sbest

if appropriate

expand sbest

end

return no possible solution

returns best “partial path” found so far

navigating in uneven terrain
Navigating in uneven terrain

Adjust relative costs of behaviors

Add non-uniform terrain costs

conclusion
Conclusion

Behavior planning approach

Organize the motion data into a data structure of high-level behaviors

Planner performs global search of behaviors to synthesize motions