SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Automated learning of muscle-actuated locomotion through control abstraction
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
NeuroAnimator: fast neural network emulation and control of physics-based models
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Composable controllers for physics-based character animation
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Precomputing avatar behavior from human motion data
Graphical Models - Special issue on SCA 2004
Responsive characters from motion fragments
ACM SIGGRAPH 2007 papers
Near-optimal character animation with continuous control
ACM SIGGRAPH 2007 papers
SIMBICON: simple biped locomotion control
ACM SIGGRAPH 2007 papers
Simulating biped behaviors from human motion data
ACM SIGGRAPH 2007 papers
Interactive simulation of stylized human locomotion
ACM SIGGRAPH 2008 papers
Contact-aware nonlinear control of dynamic characters
ACM SIGGRAPH 2009 papers
International Journal of Robotics Research
Optimizing walking controllers
ACM SIGGRAPH Asia 2009 papers
ACM SIGGRAPH Asia 2009 papers
Robust task-based control policies for physics-based characters
ACM SIGGRAPH Asia 2009 papers
Real-time planning for parameterized human motion
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Physically-based character control in low dimensional space
MIG'10 Proceedings of the Third international conference on Motion in games
Articulated swimming creatures
ACM SIGGRAPH 2011 papers
Modal-space control for articulated characters
ACM Transactions on Graphics (TOG)
Controlling physics-based characters using soft contacts
Proceedings of the 2011 SIGGRAPH Asia Conference
Injury assessment for physics-based characters
MIG'11 Proceedings of the 4th international conference on Motion in Games
Optimizing locomotion controllers using biologically-based actuators and objectives
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Video-based 3D motion capture through biped control
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review
Computer Graphics Forum
Simple data-driven control for simulated bipeds
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Physically plausible simulation for character animation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Simple data-driven control for simulated bipeds
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Physically plausible simulation for character animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Diverse motion variations for physics-based character animation
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Flexible muscle-based locomotion for bipedal creatures
ACM Transactions on Graphics (TOG)
Data-driven control of flapping flight
ACM Transactions on Graphics (TOG)
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We introduce methods for optimizing physics-based walking controllers for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. These variables are represented with probability distributions, and a return function scores the desirability of a single motion. Controller optimization entails maximizing the expected value of the return, which is computed by Monte Carlo methods. We demonstrate examples with different sources of uncertainty and task constraints. Optimizing control strategies under uncertainty increases robustness and produces natural variations in style.