Human Intention Inference Using Expectation-Maximization Algorithm With Online Model Learning

An algorithm called adaptive-neural-intention estimator (ANIE) is presented to infer the intent of a human operator’s arm movements based on the observations from a 3-D camera sensor (Microsoft Kinect). Intentions are modeled as the goal locations of reaching motions in 3-D space. Human arm’s nonlinear motion dynamics are modeled using an unknown nonlinear function with intentions represented as parameters. The unknown model is learned by using a neural network. Based on the learned model, an approximate expectation-maximization algorithm is developed to infer human intentions. Furthermore, an identifier-based online model learning algorithm is developed to adapt to the variations in the arm motion dynamics, the motion trajectory, the goal locations, and the initial conditions of different human subjects. The results of experiments conducted on data obtained from different users performing a variety of reaching motions are presented. The ANIE algorithm is compared with an unsupervised Gaussian mixture model algorithm and an Euclidean distance-based approach by using Cornell’s CAD-120 data set and data collected in the Robotics and Controls Laboratoy at UConn. The ANIE algorithm is compared with the inverse LQR and ATCRF algorithms using a labeling task carried out on the CAD-120 data set.

[1]  Antonio Bicchi,et al.  Measuring intent in human-robot cooperative manipulation , 2009, 2009 IEEE International Workshop on Haptic Audio visual Environments and Games.

[2]  G.C. Goodwin,et al.  Approximate EM Algorithms for Parameter and State Estimation in Nonlinear Stochastic Models , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[3]  K. Lange.  A gradient algorithm locally equivalent to the EM algorithm , 1995 .

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

[5]  Jodie A. Baird,et al.  Discerning intentions in dynamic human action , 2001, Trends in Cognitive Sciences.

[6]  Olaf Stursberg,et al.  Human arm motion modeling and long-term prediction for safe and efficient Human-Robot-Interaction , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  Jos Elfring,et al.  Learning intentions for improved human motion prediction , 2013, 2013 16th International Conference on Advanced Robotics (ICAR).

[8]  Jeffrey C. Trinkle,et al.  Controller design for human-robot interaction , 2008, Auton. Robots.

[9]  Brian D. Ziebart,et al.  Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation , 2015, AAAI.

[10]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[11]  Hema Swetha Koppula,et al.  Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..

[12]  Masayoshi Tomizuka,et al.  Ensuring safety in human-robot coexistence environment , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Hema Swetha Koppula,et al.  Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Luke S. Zettlemoyer,et al.  Learning to Parse Natural Language Commands to a Robot Control System , 2012, ISER.

[15]  Dana Kulic,et al.  Estimating intent for human-robot interaction , 2003 .

[16]  Warren E. Dixon,et al.  Robust Identification-Based State Derivative Estimation for Nonlinear Systems , 2013, IEEE Transactions on Automatic Control.

[17]  Siddhartha S. Srinivasa,et al.  Learning the communication of intent prior to physical collaboration , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[18]  Dmitry Berenson,et al.  Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Dmitry Berenson,et al.  Human-robot collaborative manipulation planning using early prediction of human motion , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Uwe D. Hanebeck,et al.  A generic model for estimating user intentions in human-robot cooperation , 2005, ICINCO.

[21]  Ashwin P. Dani,et al.  Learning Contracting Nonlinear Dynamics From Human Demonstration for Robot Motion Planning , 2015, HRI 2015.

[22]  V. Javier Traver,et al.  Making service robots human-safe , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[23]  Stefanos Nikolaidis,et al.  Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[24]  Monica N. Nicolescu,et al.  Understanding human intentions via Hidden Markov Models in autonomous mobile robots , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[25]  Tanja Schultz,et al.  Combined intention, activity, and motion recognition for a humanoid household robot , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[27]  Bernhard Schölkopf,et al.  Probabilistic movement modeling for intention inference in human–robot interaction , 2013, Int. J. Robotics Res..

[28]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[29]  Dmitry Berenson,et al.  A framework for unsupervised online human reaching motion recognition and early prediction , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[30]  Siddhartha S. Srinivasa,et al.  Toward seamless human-robot handovers , 2013, Journal of Human-Robot Interaction.

[31]  Gwenn Englebienne,et al.  Learning to Recognize Human Activities from Soft Labeled Data , 2014, Robotics: Science and Systems.

[32]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[33]  Ashwin P. Dani,et al.  Human intention inference and motion modeling using approximate E-M with online learning , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[34]  Roben A. Strikwerda.  Understanding Human Action: Social Explanation and the Vision of Social Science , 1991 .

[35]  A. ZelinskyResearch,et al.  The Essential Components of Human-Friendly Robot Systems , 1999 .

[36]  Carlos Morato,et al.  Toward Safe Human Robot Collaboration by Using Multiple Kinects Based Real-Time Human Tracking , 2014, J. Comput. Inf. Sci. Eng..

[37]  Michael A. Goodrich,et al.  Human-Robot Interaction: A Survey , 2008, Found. Trends Hum. Comput. Interact..

[38]  Meinard Müller,et al.  Dynamic Time Warping , 2008 .

[39]  Gwen Littlewort,et al.  Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[40]  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 .

[41]  Frank L. Lewis,et al.  Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities , 1987 .

[42]  Dana Kulic,et al.  Affective State Estimation for Human–Robot Interaction , 2007, IEEE Transactions on Robotics.

[43]  Graham C. Goodwin,et al.  Estimation with Missing Data , 1999 .

[44]  Yun Jiang,et al.  Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs , 2014, Robotics: Science and Systems.

[45]  Andrea Maria Zanchettin,et al.  Safety in human-robot collaborative manufacturing environments: Metrics and control , 2016, IEEE Transactions on Automation Science and Engineering.

[46]  Darius Burschka,et al.  Predicting human intention in visual observations of hand/object interactions , 2013, 2013 IEEE International Conference on Robotics and Automation.

[47]  Ashwin P. Dani,et al.  Human intention inference through interacting multiple model filtering , 2015, 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[48]  Warren E. Dixon,et al.  Single Camera Structure and Motion , 2012, IEEE Transactions on Automatic Control.

[49]  Warren E. Dixon,et al.  Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure , 2008, IEEE Transactions on Automatic Control.