Movement segmentation using a primitive library

Segmenting complex movements into a sequence of primitives remains a difficult problem with many applications in the robotics and vision communities. In this work, we show how the movement segmentation problem can be reduced to a sequential movement recognition problem. To this end, we reformulate the original Dynamic Movement Primitive (DMP) formulation as a linear dynamical system with control inputs. Based on this new formulation, we develop an Expectation-Maximization algorithm to estimate the duration and goal position of a partially observed trajectory. With the help of this algorithm and the assumption that a library of movement primitives is present, we present a movement segmentation framework. We illustrate the usefulness of the new DMP formulation on the two applications of online movement recognition and movement segmentation.

Authors

• Franziska Meier
• Evangelos A. Theodorou
• Freek Stulp
• Stefan Schaal

Keywords

• Trajectory
• Libraries
• Motion segmentation
• Covariance matrix
• Noise
• Equations
• Movement Of Segments
• Primitive Library
• Linear System
• Control Input
• Expectation Maximization
• Complex Movements
• Segmentation Problem
• Vision Community
• Linear Dynamical System
• Goal Position
• Help Of Algorithms
• Time Step
• Maximum Likelihood Estimation
• Parametrized
• Second Category
• Start Time
• Robotic System
• Part Of The State
• Motion Model
• Recognition Rate
• State Transition Matrix
• Action Units
• Observation Matrix
• Segmentation Points
• Highest Likelihood
• Movement Duration
• Test Instances
• Noise Covariance
• Movement Trajectories
• End-effector