1. Automated human motion in constrained environments Maciej Kalisiak mac@dgp.toronto.edu

2. Introduction human character animation constrained environments kinematic method currently 2D, extendible sample solution

3. Path Planning piano mover’s problem given: start and goal configurations find connecting path

4. Application to Human Motion

5. Approach starting point: RPP additions: –moving while in contact with environment –notion of comfort –knowledge of human gaits

6. Understanding RPP Randomized Path Planning a path planning algorithm

7. Simplest “Planner” character’s state: q repeated perturbations, i.e., Brownian motion repeat until goal reached

8. discretize into grid potential = Manhattan distance to goal flood-fill Building a Potential Field

9. Gradient Descent character  point mass sample q’s neighbourhood pick sample with largest drop in potential iterate until goal reached not feasible analytically

10. 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

11. Deep Minimum Example

12. 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 more fluid

13. Modifications grasps and grasp invariants comfort heuristic system gait finite state machine grasp-aware gradient descent, random walk, smoothing filters

14. Character Structure 10 links 9 joints 12 DOFs frequent re-rooting

15. Grasp Points represent potential points of contact three types reduce the grasp search space summarize surface characteristics

16. Grasp Invariants each gait dictates: –the number of grasps –the types of grasps enforced by the GFSM rest of planner must not alter existing grasps

17. Motion without Heuristics

18. Heuristic System each heuristic measures some quality of q D(q): overall discomfort, a potential field getting comfy: gradient descent through D(q)

19. Implemented Heuristics

20. The Gait FSM states represent gaits each edge has: –geometric preconditions –motion recipe –priority self-loops: gait-preserving motion that changes grasps

21. Complete System

22. More Results

23. Future Work 3D quadrupeds, other characters “grasp surfaces” non-limb grasping add concept of time, speed use machine learning

24. ~FIN~ http://www.dgp.toronto.edu/~mac/thesis

25. Appendix (extra slides)

26. Alternate gradient descent view

27. Smoothing Algorithm

28. Need for Limb Smoothing

29. Limb Smoothing Solution

30. Implemented GFSM

31. Contributions human character animation algorithm for constrained environments –grasp point discretization of environment –grasp constraint –comfort modeling using heuristics –gait FSM –adapted RPP algorithms to grasp constraint