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Automated human motion in constrained environments

A kinematic method for human character animation in 2D constrained environments, with extendible solutions. Applies randomized path planning and heuristic systems to optimize motion comfort and knowledge of human gaits.

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Automated human motion in constrained environments

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

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

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

  4. Application to Human Motion

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

  6. Randomized Path Planning a path planning algorithm Understanding RPP

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

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

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

  10. 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

  11. Deep Minimum Example

  12. 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 Smoothing

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

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

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

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

  17. Motion without Heuristics

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

  19. Implemented Heuristics

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

  21. Complete System

  22. More Results

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

  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. 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

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