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Precomputed Search Trees: Planning for Interactive Goal-Driven
Animation
Manfred Lau and James KuffnerCarnegie Mellon University
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Motion Planning approach
Inputs Output
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Behavior Planner
Lau and Kuffner. “Behavior Planning for Character Animation.” SCA 05
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Motivation
Efficient algorithm for: large number of characters global planning re-plan continuously in real-
time dynamic environment complex motions including
jump, crawl, duck, stop-and-wait
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Main contribution
Precomputed Search Tree
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Traditional Planning
~50,000 μs for 1 s of motion
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Precomputed Search Trees
~250 μs for 1 s of motion
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Overview
FSM
Environment
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Overview
Precompute
FSM
Environment
1) Search Tree
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Overview
FSM
Environment
Precompute
2) Gridmaps
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Overview
FSM
EnvironmentPrecompute
1) Search Tree
2) Gridmaps
Runtime
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Overview
FSM
EnvironmentPrecompute
1) Search Tree
2) Gridmaps
Runtime
1) Map Obstacles
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Overview
FSM
EnvironmentPrecompute
1) Search Tree
2) Gridmaps
Runtime
1) Map Obstacles
2) Path Finding
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Overview – Distant Goal
Coarse-Level Planner
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Overview – Distant Goal
Coarse-Level Planner Repeatedly select sub-goaland run each sub-case
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Related Work
Motion PlanningKuffner 98Shiller et al. 01Bayazit et al. 02Choi et al. 03Pettre et al. 03Sung et al. 05
Koga et al. 94Kalisiak and van de Panne 01Yamane et al. 04
Choi et al. 03GlobalNavigation
Manipulation andwhole-body motions
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Related Work
PrecomputationLee and Lee 04Reitsma and Pollard 04
Re-playing original motion capture dataArikan and Forsyth 02Kovar et al. 02Lee et al. 02Pullen and Bregler 02Gleicher et al. 03Lee et al. 06
Lee and Lee 04
Kovar et al. 02
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Related Work
Motion Vector Fields / Steering ApproachesBrogan and Hodgins 97Menache 99Reynolds 99Mizuguchi et al. 01Treuille et al. 06
Treuille et al. 06
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Advantages of our approach
Precomputed Search Trees: many characters re-plan
continuously in real-time global planning – as
opposed to local policy methods
complex motions – jump, crawl, duck, stop-and-wait
one tree – can be used for all characters, and different environments
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Environment Representation
Obstacle Growth in Robot Path PlanningUdupa 77Lozano-Pérez and Wesley 83
Special regionsfor crawl/jump
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Behavior Finite-State Machine
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Precompute
1) Search Tree
2 levels, 3 behavior states
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Precompute
1) Search Tree
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Precompute
1) Search Treerepresents all states reachable from current state
5 levels, 7 behavior states
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Precompute
1) Search Tree – Pruned to ~10 MB
exhaustive pruned
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Precompute
2) Environment Gridmapused to identify the tree nodes that are blocked by obstacles
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Precompute
2) Goal Gridmapused to efficiently extract all paths that reach goal from start state
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Runtime
1) Map obstacles to Environment Gridmap
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Runtime
1) Map obstacles to Environment Gridmap
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Runtime
2) Path Finding – reverse path lookup (vs. forward search)
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Runtime2) Path Finding – take shortest path that reaches goal
21
3
root
obstacle
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Runtime2) Path Finding – take shortest path that reaches goal
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Runtime2) Path Finding – take shortest path that reaches goal
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Motion Generation / Blending
Sequence of behaviors converted to actual motion
Blending at frames near transition pointsLinearly interpolate root positionsSmooth-in, smooth-out slerp interpolation for joint rotations
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Planning to distant goals
Only up to specific level
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Intermediate goal points
Apply precomputed tree repeatedly
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Intermediate goal points
Apply precomputed tree repeatedly
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Intermediate goal points
Apply precomputed tree repeatedly
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Distant goal example
Run coarse bitmap planner first
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Distant goal example
Find sub-goal Run sub-case
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Distant goal example
Find sub-goal Run sub-case
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Distant goal example
Final solution
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Distant goal example
Final solution
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Result – speedup
Precomputed Trees A*-search
Avg. runtime or 3,131 550,591search time (μs) 176 times faster
Avg μs per frame 7.95 1,445Avg pathcost 361 357
Avg time of synthesized 13,123,333 12,700,000motion (μs)
Real-time speedup 4,191 times 23 times
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Effect of using less memory for Precomputed Tree
80
85
90
95
100
105
110
115
120
1 10 100 1000
Memory for Precomputed Tree (MB)
Pat
h C
ost
(N
orm
aliz
ed t
o 1
00)
Tradeoff: Motion Quality vs. Memory
exhaustive tree
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Single Character Mode
complete solution path for one character continuously re-generated, as the user changes environment
large environment (70 by 70 meters), takes 6 ms to generate full path
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Multiple Character Mode
execute “runtime path finding” phase only after we start rendering the first frame from the previous partial path
precompute blend frames (~20 motion clips), precompute all pairs
separate gridmaps for collision avoidance between characters
same precomputed tree for all characters
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Summary
Advantages of our approach: large number of characters global planning re-plan continuously in real-
time complex environment complex motions
Precomputed Search Tree
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Summary
Advantages of our approach: large number of characters global planning re-plan continuously in real-
time complex environment complex motions
Precomputed Search Tree
Thank you! Questions.