Probabilistic movement modeling for intention inference in human–robot interaction

Intention inference can be an essential step toward efficient human–robot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows the intention to be inferred from observed movements using Bayes’ theorem. The IDDM simultaneously finds a latent state representation of noisy and high-dimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human–robot interaction scenarios, i.e. target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

[1]  M. A. Simon,et al.  Understanding Human Action: Social Explanation and the Vision of Social Science. , 1983 .

[2]  Russell L. Anderson,et al.  A Robot Ping-Pong Player: Experiments in Real-Time Intelligent Control , 1988 .

[3]  John T. Wen,et al.  A robot ping pong player: optimized mechanics, high performance 3D vision, and intelligent sensor control , 1990, Robotersysteme.

[4]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[5]  Max Donath,et al.  American Control Conference , 1993 .

[6]  Zoubin Ghahramani,et al.  Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.

[7]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[8]  A. Williams,et al.  Anticipation skill in a real-world task: measurement, training, and transfer in tennis. , 2002, Journal of experimental psychology. Applied.

[9]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[10]  Agathe Girard,et al.  Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[11]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[12]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[13]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[14]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[15]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[16]  Joshua B. Tenenbaum,et al.  Bayesian models of human action understanding , 2005, NIPS.

[17]  Fumio Miyazaki,et al.  A learning approach to robotic table tennis , 2005, IEEE Transactions on Robotics.

[18]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[19]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[20]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[21]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[22]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[23]  Rajesh P. N. Rao,et al.  Imitation and Social Learning in Robots, Humans and Animals: A Bayesian model of imitation in infants and robots , 2007 .

[24]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[25]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[26]  Odest Chadwicke Jenkins,et al.  Interactive Human Pose and Action Recognition Using Dynamical Motion Primitives , 2007, Int. J. Humanoid Robotics.

[27]  Christian Laugier,et al.  Intentional motion on-line learning and prediction , 2008, Machine Vision and Applications.

[28]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[29]  Dieter Fox,et al.  GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[31]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[32]  David Hsu,et al.  Motion planning under uncertainty for robotic tasks with long time horizons , 2010, Int. J. Robotics Res..

[33]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[34]  Siddhartha S. Srinivasa,et al.  Planning-based prediction for pedestrians , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  Uwe D. Hanebeck,et al.  Analytic moment-based Gaussian process filtering , 2009, ICML '09.

[36]  Christian Laugier,et al.  Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion , 2009, Int. J. Robotics Res..

[37]  Chris L. Baker,et al.  Action understanding as inverse planning , 2009, Cognition.

[38]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[39]  Sethu Vijayakumar,et al.  Latent spaces for dynamic movement primitives , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[40]  De Xu,et al.  Control system design for a 5-DOF table tennis robot , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[41]  Jan Peters,et al.  A biomimetic approach to robot table tennis , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[42]  Marc Peter Deisenroth,et al.  Efficient reinforcement learning using Gaussian processes , 2010 .

[43]  Carl E. Rasmussen,et al.  State-Space Inference and Learning with Gaussian Processes , 2010, AISTATS.

[44]  Christoph H. Lampert,et al.  Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components , 2012, Journal of Real-Time Image Processing.

[45]  Rajesh P. N. Rao,et al.  Gaze Following as Goal Inference: A Bayesian Model , 2011, CogSci.

[46]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[47]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[48]  Dieter Fox,et al.  Learning GP-BayesFilters via Gaussian process latent variable models , 2009, Auton. Robots.

[49]  Christoph H. Lampert,et al.  Learning anticipation policies for robot table tennis , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[50]  Jan Peters,et al.  Balancing Safety and Exploitability in Opponent Modeling , 2011, AAAI.

[51]  M. Deisenroth,et al.  A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms , 2011, Proceedings of the 2011 American Control Conference.

[52]  Neil D. Lawrence,et al.  Variational Gaussian Process Dynamical Systems , 2011, NIPS.

[53]  Carl E. Rasmussen,et al.  Robust Filtering and Smoothing with Gaussian Processes , 2012, IEEE Transactions on Automatic Control.

[54]  Oliver Kroemer,et al.  Learning to select and generalize striking movements in robot table tennis , 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.

[55]  Emilio Frazzoli,et al.  Intention-Aware Motion Planning , 2013, WAFR.

[56]  David Hsu,et al.  Monte Carlo Bayesian Reinforcement Learning , 2012, ICML.

[57]  Anind K. Dey,et al.  Probabilistic pointing target prediction via inverse optimal control , 2012, IUI '12.

[58]  Wolfram Burgard,et al.  Feature-Based Prediction of Trajectories for Socially Compliant Navigation , 2012, Robotics: Science and Systems.

[59]  Mohammad Emtiyaz Khan,et al.  A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models , 2012, AISTATS.

[60]  Bernhard Schölkopf,et al.  Probabilistic Modeling of Human Movements for Intention Inference , 2012, Robotics: Science and Systems.

[61]  De Xu,et al.  Adding Active Learning to LWR for Ping-Pong Playing Robot , 2013, IEEE Transactions on Control Systems Technology.

[62]  Kris K. Hauser,et al.  Recognition, prediction, and planning for assisted teleoperation of freeform tasks , 2012, Autonomous Robots.