Performance-Based Biped Control using a Consumer Depth Camera

Yoonsang Lee, Taesoo Kwon
Eurographics 2017

A user controls a physically simulated character by moving his or her body parts. Top: The input depth data of user poses. Bottom: The simulated character imitating the user’s actions.

Abstract
We present a technique for controlling physically simulated characters using user inputs from an off-the-shelf depth camera. Our controller takes a real-time stream of user poses as input, and simulates a stream of target poses of a biped based on it. The simulated biped mimics the user's actions while moving forward at a modest speed and maintaining balance. The controller is parameterized over a set of modulated reference motions that aims to cover the range of possible user actions. For real-time simulation, the best set of control parameters for the current input pose is chosen from the parameterized sets of pre-computed control parameters via a regression method. By applying the chosen parameters at each moment, the simulated biped can imitate a range of user actions while walking in various interactive scenarios.

Paper

Download : pdf (7MB)

Video

Download : mp4 (67MB)

Slides
Eurographics 2017 talk slides : pptx (193MB) or pdf (2MB)

Push-Recovery Stability of Biped Locomotion

Yoonsang Lee, Kyungho Lee, Soon-Sun Kwon, Jiwon Jeong, Carol O'Sullivan, Moon Seok Park, Jehee Lee
SIGGRAPH Asia 2015

 
Motion capture of our push-recovery experiment. The experimenter pushed the participant while walking and measured lateral detour from the straight line.

Abstract
Biped controller design pursues two fundamental goals; simulated walking should look human-like and robust against perturbation while maintaining its balance. Normal gait is a pattern of walking that humans normally adopt in undisturbed situations. It has previously been postulated that normal gait is more energy efficient than abnormal or impaired gaits. However, it is not clear whether normal gait is also superior to abnormal gait patterns with respect to other factors, such as stability. Understanding the correlation between gait and stability is an important aspect of biped controller design. We studied this issue in two sets of experiments with human participants and a simulated biped. The experiments evaluated the degree of resilience to external pushes for various gait patterns. We identified four gait factors that affect the balance-recovery capabilities of both human and simulated walking. We found that crouch gait is significantly more stable than normal gait against lateral push. Walking speed and the timing/magnitude of disturbance also affect gait stability. Our work would provide a potential way to  compare the performance of biped controllers by normalizing their output gaits and improve their performance by adjusting these decisive factors.

Paper

Download : pdf (5.3MB)

Video

Download : mp4 (69.3MB)

Slides
SIGGRAPH Asia 2015 talk slides : pptx (127MB)

Data
Human & simulation measurement data : zip (1.2MB) 
Motion capture data from the human experiments : zip (99.1MB) 

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)