Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories

  title={Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories},
  author={Julen Urain and Jan Peters},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  • Julen Urain, Jan Peters
  • Published 2019
  • Computer Science, Mathematics
  • 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Similarity distance measure between two trajectories is an essential tool to understand patterns in motion, for example, in Human-Robot Interaction or Imitation Learning. [...] Key Method Based on Pearson’s Correlation Coefficient and the Coefficient of Determination, our similarity metric, the Generalized Multiple Correlation Coefficient (GMCC) is presented like the natural extension of the Multiple Correlation Coefficient. The motivation of this paper is two-fold: First, to introduce a new correlation metric…Expand
Robust Multivariate Correlation Techniques: A Confirmation Analysis using Covid-19 Data Set
Robust multivariate correlation techniques are proposed to determine the strength of the association between two or more variables of interest since the existing multivariate correlation techniquesExpand
Learning Human-like Hand Reaching for Human-Robot Handshaking
A novel framework for learning human-robot handshaking behaviours for humanoid robots solely using third-person human-human interaction data is presented, especially useful for non-backdrivable robots that cannot be taught by demonstrations via kinesthetic teaching. Expand
Evaluation of the Handshake Turing Test for anthropomorphic Robots
This work proposes an initial Turing-like test for the hardware interface to future AI agents to test the human-likeness of a robot handshake and proposes some modifications to the definition of a Turing test for such scenarios taking into account that a human needs to interact with a physical medium. Expand


Robust and fast similarity search for moving object trajectories
Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences, indicate that EDR is more robust than Euclideans distance, DTW and ERP, and it is on average 50% more accurate than LCSS. Expand
Brownian distance covariance
Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlationExpand
Relations Between Two Sets of Variates
Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots atExpand
Probabilistic Movement Primitives
This work analytically derive a stochastic feedback controller which reproduces the given trajectory distribution for robot movement control and presents a probabilistic formulation of the MP concept that maintains a distribution over trajectories. Expand
Adaptation and Robust Learning of Probabilistic Movement Primitives
This article makes use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances, and introduces general purpose operators to adapt movement primitives in joint and task space. Expand
Computing Discrete Fréchet Distance ∗
The Fréchet distance between two curves in a metric space is a measure of the similarity between the curves. We present a discrete variation of this measure. It provides good approximations of theExpand
Toward accurate dynamic time warping in linear time and space
This paper introduces FastDTW, an approximation of DTW that has a linear time and space complexity and shows a large improvement in accuracy over existing methods. Expand
Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives
An approach to endow the iCub with basic capabilities of intention recognition, based on Probabilistic Movement Primitives (ProMPs), a versatile method for representing, generalizing, and reproducing complex motor skills is proposed. Expand
Learning interaction for collaborative tasks with probabilistic movement primitives
This paper introduces the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. Expand
Time Warp Edit Distance
This family of time warp distances is constructed as an editing distance whose elementary operations apply on linear segments that is well suited for the processing of event data for which each data sample is associated with a timestamp, not necessarily obtained according to a constant sampling rate. Expand