A Grasp-based Motion Planning Algorithm for Intelligent Character Animation

1. A Grasp-based Motion Planning Algorithm for Intelligent Character Animation Maciej Kalisiak mac@dgp.toronto.edu

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

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

4. freespace motion planning piano mover’s problem example RPP solution Randomized Path Planning (RPP)

5. 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 Combined Approach

6. character’s state repeated perturbations,i.e., Brownian motion perturbations move COM inefficient Simplest “Planner”

7. 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) Potential-guided Planner

8. 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 Local Minima

9. Deep Minimum Example

10. 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 Smoothing

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

12. represent potential points of contact reduces the grasp search space “grasp”: attachment of limb to grasp point three types Grasp Points

13. 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) Grasp Constraint

14. 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 The Gait FSM

15. 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) Heuristic System

16. Complete System

17. Results

18. 3D grasp surfaces arbitrary, non-human skeletons complex grasping motion speed control learning Future Work

19. 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 Contributions

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