Locomotion Control for Many-Muscle Humanoids

Yoonsang Lee, Moon Seok Park, Taesoo Kwon, Jehee Lee
SIGGRAPH Asia 2014

Abstract
We present a biped locomotion controller for humanoid models actuated by more than a hundred Hill-type muscles. The key component of the controller is our novel algorithm that can cope with step-based biped locomotion balancing and the coordination of many nonlinear Hill-type muscles simultaneously. Minimum effort muscle activations are calculated based on muscle contraction dynamics and online quadratic programming. Our controller can faithfully reproduce a variety of realistic biped gaits (e.g., normal walk, quick steps, and fast run) and adapt the gaits to varying conditions (e.g., muscle weakness, tightness, joint dislocation, and external pushes) and goals (e.g., pain reduction and efficiency maximization). We demonstrate the robustness and versatility of our controller with examples that can only be achieved using highly-detailed musculoskeletal models with many muscles.

Paper

Download : pdf (12.0MB)

Video

Download : mp4 (55.3MB)


Supplemental Material
Download : pdf (1.0MB)

Slides
SIGGRAPH Asia 2014 talk slides : pptx (101.5MB)

Data
Humanoid models & Reference motions : zip (8.2MB) 

Data-Driven Biped Control

Yoonsang Lee, Sungeun Kim, Jehee Lee

SIGGRAPH 2010

Our data-driven controller allows the physically-simulated biped character to reproduce challenging motor skills captured in motion data. 

Abstract

We present a dynamic controller to physically simulate under-actuated three-dimensional full-body biped locomotion. Our data-driven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-the-fly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively.

Paper

Download : pdf (1.4MB)

Video

Full video : mov (60.2MB)

Spinning example : 
- original speed - mov (1.2MB)
- 1/2 speed - mov (2.5MB)
- 1/3 speed - mov (3.8MB)
- 1/4 speed - mov (4.8MB)

Slides
SIGGRAPH 2010 talk slides : pptx (2.2MB, without video) / zip (132MB, with video)

Data
Reference motion capture data : zip (0.7MB)