Deriving action and behavior primitives from human motion data

We address the problem of creating basis behaviors for modularizing humanoid robot control and representing human activity. These behaviors, called perceptual-motor primitives, serve as a substrate for linking a system's perception of human activities and the ability to perform those activities. We present a data-driven method for deriving perceptual-motor action and behavior primitives from human motion capture data. In order to find these primitives, we employ a spatio-temporal non-linear dimension reduction technique on a set of motion segments. From this transformation, motions representing the same action can be clustered and generalized. Further dimension reduction iterations are applied to derive extended-duration behaviors.

[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]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Mitsuo Kawato,et al.  Multiple Paired Forward-Inverse Models for Human Motor Learning and Control , 1998, NIPS.

[5]  Donald P. Greenberg,et al.  The Irradiance Volume , 1998, IEEE Computer Graphics and Applications.

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

[7]  S. Schaal,et al.  Programmable Pattern Generators , 1998 .

[8]  Maja J. Mataric,et al.  Movement control methods for complex, dynamically simulated agents: Adonis dances the Macarena , 1998, AGENTS '98.

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

[10]  Michael Cohen,et al.  Shape and Animation by Example , 2000 .

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

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

[13]  Maja J. Matarić,et al.  Primitive-Based Movement Classification for Humanoid Imitation , 2000 .

[14]  Terrence J. Sejnowski,et al.  Nonlinear blind source separation using kernel feature spaces , 2001 .

[15]  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).

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

[17]  Chrystopher L. Nehaniv,et al.  Sensory-Motor Primitives as a Basis for Imitation: Linking Perception to Action and Biology to Robotics , 2002 .