Automated derivation of behavior vocabularies for autonomous humanoid motion

In this paper we address the problem of automatically deriving vocabularies of motion modules from human motion data, taking advantage of the underlying spatio-temporal structure in motion. We approach this problem with a data-driven methodology for modularizing a motion stream (or time-series of human motion) into a vocabulary of parameterized primitive motion modules and a set of meta-level behaviors characterizing extended combinations of the primitives. Central to this methodology is the discovery of spatio-temporal structure in a motion stream. We estimate this structure by extending an existing nonlinear dimension reduction technique, Isomap, to handle motion data with spatial and temporal dependencies. The motion vocabularies derived by our methodology provide a substrate of autonomous behavior and can be used in a variety of applications. We demonstrate the utility of derived vocabularies for the application of synthesizing new humanoid motion that is structurally similar to the original demonstrated motion.

[1]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[2]  Dinesh Manocha,et al.  I-COLLIDE: an interactive and exact collision detection system for large-scale environments , 1995, I3D '95.

[3]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[4]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[5]  Michael F. Cohen,et al.  Verbs and Adverbs: Multidimensional Motion Interpolation , 1998, IEEE Computer Graphics and Applications.

[6]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[7]  Daniel Thalmann,et al.  ACE: A Platform for the Real Time Simulation of Virtual Human Agents , 2000, Computer Animation and Simulation.

[8]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[9]  Aaron Hertzmann,et al.  Style machines , 2000, SIGGRAPH 2000.

[10]  Mohan M. Trivedi,et al.  Articulated body posture estimation from multi-camera voxel data , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Christopher G. Atkeson,et al.  Learning from observation using primitives , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[12]  Jun Nakanishi,et al.  Trajectory formation for imitation with nonlinear dynamical systems , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[13]  Maja J. Mataric,et al.  Automated Derivation of Primitives for Movement Classification , 2000, Auton. Robots.

[14]  Harry Shum,et al.  Motion texture: a two-level statistical model for character motion synthesis , 2002, ACM Trans. Graph..

[15]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

[16]  Randall W. Hill,et al.  Toward a New Generation of Virtual Humans for Interactive Experiences , 2002, IEEE Intell. Syst..

[17]  Maja J. Matarić,et al.  Converting Sequences of Human Volumes into Kinematic Motion , 2002 .

[18]  Okan Arikan,et al.  Interactive motion generation from examples , 2002, ACM Trans. Graph..

[19]  Lucas Kovar,et al.  Motion graphs , 2002, SIGGRAPH '08.

[20]  Gordon Cheng,et al.  Humanoid robot learning and game playing using PC-based vision , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Maja J. Matarić,et al.  Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics , 2002 .

[22]  Maja J. Mataric,et al.  Markerless kinematic model and motion capture from volume sequences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..