automated human motion in constrained environments maciej kalisiak mac@dgp.toronto.edu

Automated human motion in constrained environments

Maciej Kalisiakmac@dgp.toronto.edu

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~

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

Appendix

(extra slides)

Alternate gradient descent view

Smoothing Algorithm

Need for Limb Smoothing

Limb Smoothing Solution

Implemented GFSM

Contributions

• 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