Automated human motion in constrained environments Maciej Kalisiak

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

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

Application to Human Motion

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

Understanding RPP Randomized Path Planning a path planning algorithm

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

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

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

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

Deep Minimum Example

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

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

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

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

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

Motion without Heuristics

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

Implemented Heuristics

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

Complete System

More Results

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

~FIN~

Appendix (extra slides)

Alternate gradient descent view

Smoothing Algorithm

Need for Limb Smoothing

Limb Smoothing Solution

Implemented GFSM

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