Online Control of Simulated Humanoids Using Particle Belief Propagation

Perttu Hämäläinen1 Joose Rajamäki1 C. Karen Liu2

1Aalto University 2Georgia Tech

To appear in Proc. SIGGRAPH 2015.

Part of the Future Game Animation project.

Our algorithm can handle complex balancing and manipulation tasks while adapting to user interactions. All our demonstrated movements emerge from simple cost functions without animation data or offline precomputation.


We present a novel, general-purpose Model-Predictive Control (MPC) algorithm that we call Control Particle Belief Propagation (C-PBP). C-PBP combines multimodal, gradient-free sampling and a Markov Random Field factorization to effectively perform simultaneous path finding and smoothing in high-dimensional spaces. We demonstrate the method in online synthesis of interactive and physically valid humanoid movements, including balancing, recovery from both small and extreme disturbances, reaching, balancing on a ball, juggling a ball, and fully steerable locomotion in an environment with obstacles. Such a large repertoire of movements has not been demonstrated before at interactive frame rates, especially considering that all our movement emerges from simple cost functions. Furthermore, we abstain from using any precomputation to train a control policy offline, reference data such as motion capture clips, or state machines that break the movements down into more manageable subtasks. Operating under these conditions enables rapid and convenient iteration when designing the cost functions.



  Title                    = {Online Control of Simulated Humanoids Using Particle Belief Propagation},
  Author                   = {H{\"a}m{\"a}l{\"a}inen, Perttu and Rajam{\"a}ki, Joose and Liu, C. Karen},
  Booktitle                = {Proc. {SIGGRAPH} '15},
  Year                     = {2015},
  Address                  = {New York, {NY}, {USA}},
  Publisher                = {{ACM}},
  Optseries                = {{SIGGRAPH} '15},

Perttu Hämäläinen, May 6 2015