1. A Grasp-Based Motion Planning Algorithm for Character Animation M. Kalisiak, M. van de Panne Eurographics Workshop on Computer Animation & Simulation 2000

2. Our Goals  Human character animation  Complex environment  Physical correctness  Aesthetic constraints

3. Solution

4. Outline  A precursory planner  The gradient descent method  Escaping local minima  The complete planner  Grasp points  Posture heuristics  Trajectory smoothing  Locomotion modes  Results and Future Work

5. The Gradient Descent Method P(q) = shortest distance to goal P(q) cannot be easily computed For each configuration q :  sample a neighborhood of q  select sample that results in largest drop of P(q) q goal q init

6. The Gradient Descent Method P(q) = shortest distance to goal P(q) cannot be easily computed For each configuration q :  sample a neighborhood of q  select sample that results in largest drop of P(q) q goal q init

7. The Gradient Descent Method P(q) = shortest distance to goal P(q) cannot be easily computed For each configuration q :  sample a neighborhood of q  select sample that results in largest drop of P(q) q goal q init

8. The Gradient Descent Method P(q) = shortest distance to goal P(q) cannot be easily computed For each configuration q :  sample a neighborhood of q  select sample that results in largest drop of P(q) q goal q init

9. Escaping Local Minima Gradient descent method stops at any minimum Solution #1: Random walks Apply Brownian motion for a given duration Solution #2: Backtracking  Restart planner at an earlier point q in solution trajectory  Sample a neighborhood of q  Select any sample as the next configuration

10. Escaping Local Minima - Example

11. Limitations  No knowledge of human postures  No physical correctness

12. Outline  A precursory planner  The gradient descent method  Random walks and backtracking  The complete planner  Grasp points  Posture heuristics  Trajectory smoothing  Locomotion modes  Results and Future Work

13. Grasp Points  Represent potential contacts with surrounding obstacles  Reduce number of ways a character can interact with the environment

14. The Walking Cycle a) Starting posture b) After a few gradient descent steps c) Next grasp point reached with inverse kinematics d) Grasp switched to other leg

15. Posture Heuristics Problem: Motion does not look natural Solution:  Each posture is rated by a set of heuristics  Each posture is iteratively corrected, until it rates well

16. Discomfort function D(q) = global rate given to posture q by the set of heuristics Use gradient descent method to minimize D(q):  Sample a neighborhood of q  Select sample that results in largest drop of D(q) Posture Correction

17. Body Trajectory Smoothing  Cull unwanted motion segments (noise)  Achieve natural fluid motion Smoothing Algorithm:  Try to replace path with linear interpolation  If collision, subdivide path. Then, repeat the process for each resulting sub-path  Use inverse kinematics to preserve grasp points

18. Limb Trajectory Smoothing  Limbs that are not involved in grasping exhibit unnecessary movement  Use separate linear interpolation for each of these limbs

19. Locomotion modes Walking CrawlingSwinging Climbing

20. Finite State Machine  Prevents haphazard motion  States dictates which heuristics can be used  Edges represent state transitions  Edges carry a set of preconditions and effects

21. Complete System  Gradient descent method determines overall trajectory  New grasp points are chosen when reachable  Current state dictates which heuristics can be used  Posture is corrected with heuristics  Trajectory is smoothed for better realism

22. Outline  A precursory planner  The gradient descent method  Random walks and backtracking  The complete planner  Posture heuristics  Trajectory smoothing  Locomotion modes  Results and Future Work

23. Results

25. Future work  Three dimensions  Grasp surfaces  Arbitrary skeletons  Motion speed control  Complex grasping  Machine learning  Keyframe timing relaxation