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A Grasp-based Motion Planning Algorithm for Intelligent Character Animation

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A Grasp-based Motion Planning Algorithm for Intelligent Character Animation Maciej Kalisiak mac_at_dgp.toronto.edu – PowerPoint PPT presentation

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Title: A Grasp-based Motion Planning Algorithm for Intelligent Character Animation


1
A Grasp-based Motion Planning Algorithm for
Intelligent Character Animation
  • Maciej Kalisiak
  • mac_at_dgp.toronto.edu

2
Introduction
  • human character animation
  • constrained environments
  • example problem
  • related research areas

3
Animation Techniques
  • many methods
  • motion capture
  • specific gait models
  • handcrafted controllers
  • spacetime constraints
  • etc.
  • cannot solve our problem

4
Randomized Path Planning (RPP)
  • freespace motion planning
  • piano movers problem
  • example RPP solution

5
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

6
Simplest Planner
  • characters state
  • repeated perturbations,i.e., Brownian motion
  • perturbations move COM
  • inefficient

7
Potential-guided Planner
  • P(q) COMs shortest distance to goal
  • solve using gradient descent
  • analytic gradient computation not feasible
  • repeatedly sample qs neighbourhood and choose
    perturbations that result in largest drop in P(q)

8
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

9
Deep Minimum Example
10
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

11
Our Extensions
  • grasp points
  • grasp constraint
  • comfort heuristic system
  • gait finite state machine
  • adapted gradient descent, random walk, smoothing
    filters

12
Grasp Points
  • represent potential points of contact
  • reduces the grasp search space
  • grasp attachment of limb to grasp point
  • three types

13
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)

14
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

15
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)

16
Complete System
17
Results
18
Future Work
  • 3D
  • grasp surfaces
  • arbitrary, non-human skeletons
  • complex grasping
  • motion speed control
  • learning

19
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

20
FIN
MPEG movies of results available
at http//www.dgp.toronto.edu/mac/thesis
21
Appendix
  • (extra slides that might prove useful in
    answering questions)

22
Character Structure
23
bitmap and distance map
24
Alternate gradient descent view
25
Motion without Heuristics
26
Smoothing Algorithm
27
Need for Limb Smoothing
28
Limb Smoothing Solution
29
Implemented GFSM
30
Implemented Heuristics
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