Learning from Natural Human Interactions for Assistive Robots

[1]  Moritz Tenorth,et al.  A Knowledge Processing Service for Robots and Robotics/AI Researchers , 2015, ICRA 2015.

[2]  Tara N. Sainath,et al.  Deep Belief Networks using discriminative features for phone recognition , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[4]  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.

[5]  Lydia E. Kavraki,et al.  The Open Motion Planning Library , 2012, IEEE Robotics & Automation Magazine.

[6]  Markus Enzweiler,et al.  Will this car change the lane? - Turn signal recognition in the frequency domain , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

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

[8]  Thierry Siméon,et al.  Sampling-Based Path Planning on Configuration-Space Costmaps , 2010, IEEE Transactions on Robotics.

[9]  Raffaello D'Andrea,et al.  Guest editorial: A revolution in the warehouse: a retrospective on Kiva Systems and the grand challenges ahead , 2012, IEEE Trans Autom. Sci. Eng..

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

[11]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[12]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[13]  Yoshua Bengio,et al.  Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models , 1993, NIPS.

[14]  Steen Kristensen,et al.  Toward interactive learning for manufacturing assistants , 2003, IEEE Trans. Ind. Electron..

[15]  Trevor Darrell,et al.  Latent-Dynamic Discriminative Models for Continuous Gesture Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Alexander G. Hauptmann,et al.  MoSIFT: Recognizing Human Actions in Surveillance Videos , 2009 .

[17]  Hema Swetha Koppula,et al.  RoboBrain: Large-Scale Knowledge Engine for Robots , 2014, ArXiv.

[18]  Ruzena Bajcsy,et al.  Safe semi-autonomous control with enhanced driver modeling , 2012, 2012 American Control Conference (ACC).

[19]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[20]  Sven Horstmann,et al.  Towards interactive learning for manufacturing assistants , 2001, Proceedings 10th IEEE International Workshop on Robot and Human Interactive Communication. ROMAN 2001 (Cat. No.01TH8591).

[21]  Thorsten Joachims,et al.  Learning preferences for manipulation tasks from online coactive feedback , 2015, Int. J. Robotics Res..

[22]  Larry S. Davis,et al.  Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Manuela M. Veloso,et al.  Conditional random fields for activity recognition , 2007, AAMAS '07.

[24]  Cordelia Schmid,et al.  Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.

[25]  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 .

[26]  Fernando De la Torre,et al.  Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision , 2014, ArXiv.

[27]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[28]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[29]  Nathan Ratliff,et al.  Online) Subgradient Methods for Structured Prediction , 2007 .

[30]  Trevor Darrell,et al.  Hidden Conditional Random Fields for Gesture Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[31]  Seth Hutchinson,et al.  Using manipulability to bias sampling during the construction of probabilistic roadmaps , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[32]  Fei-Fei Li,et al.  Visualizing and Understanding Recurrent Networks , 2015, ArXiv.

[33]  Razvan Pascanu,et al.  Theano: new features and speed improvements , 2012, ArXiv.

[34]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[35]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[36]  Vijay Kumar,et al.  Identification and Representation of Homotopy Classes of Trajectories for Search-based Path Planning in 3D , 2011, Robotics: Science and Systems.

[37]  Antoni B. Chan,et al.  Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[38]  Pieter Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2010, Int. J. Robotics Res..

[39]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[40]  Yoshua Bengio,et al.  Global training of document processing systems using graph transformer networks , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[41]  Ramakant Nevatia,et al.  ACTIVE: Activity Concept Transitions in Video Event Classification , 2013, 2013 IEEE International Conference on Computer Vision.

[42]  Maxim Likhachev,et al.  Search-based planning for manipulation with motion primitives , 2010, 2010 IEEE International Conference on Robotics and Automation.

[43]  Matei T. Ciocarlie,et al.  Interactive Markers: 3-D User Interfaces for ROS Applications [ROS Topics] , 2011, IEEE Robotics Autom. Mag..

[44]  Maria E. Jabon,et al.  Facial expression analysis for predicting unsafe driving behavior , 2011, IEEE Pervasive Computing.

[45]  Siddhartha S. Srinivasa,et al.  Legibility and predictability of robot motion , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[46]  Mohan M. Trivedi,et al.  Looking-in and looking-out vision for Urban Intelligent Assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[47]  Ramakant Nevatia,et al.  Key Object Driven Multi-category Object Recognition, Localization and Tracking Using Spatio-temporal Context , 2008, ECCV.

[48]  Yoshua Bengio,et al.  On the Expressive Power of Deep Architectures , 2011, ALT.

[49]  Jussi Rintanen,et al.  Planning as satisfiability: Heuristics , 2012, Artif. Intell..

[50]  Rachid Alami,et al.  A Human Aware Mobile Robot Motion Planner , 2007, IEEE Transactions on Robotics.

[51]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[52]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[53]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  Klaus C. J. Dietmayer,et al.  Continuous Driver Intention Recognition with Hidden Markov Models , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[55]  Trevor Darrell,et al.  Open-vocabulary Object Retrieval , 2014, Robotics: Science and Systems.

[56]  Jayesh K. Gupta,et al.  PlanIt: A crowdsourcing approach for learning to plan paths from large scale preference feedback , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[57]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[58]  Hedvig Kjellström,et al.  Recognizing object affordances in terms of spatio-temporal object-object relationships , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[59]  Nathan Ratliff,et al.  Learning to search: structured prediction techniques for imitation learning , 2009 .

[60]  Ashutosh Saxena,et al.  Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds , 2015, ISRR.

[61]  Thorsten Joachims,et al.  Online Structured Prediction via Coactive Learning , 2012, ICML.

[62]  Pieter Abbeel,et al.  Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization , 2013, Robotics: Science and Systems.

[63]  Erich Elsen,et al.  Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.

[64]  Yun Jiang,et al.  Learning to place new objects in a scene , 2012, Int. J. Robotics Res..

[65]  Manuel Lopes,et al.  Learning Object Affordances: From Sensory--Motor Coordination to Imitation , 2008, IEEE Transactions on Robotics.

[66]  Sergey Levine,et al.  Continuous Inverse Optimal Control with Locally Optimal Examples , 2012, ICML.

[67]  Pieter Abbeel,et al.  Autonomous Helicopter Aerobatics through Apprenticeship Learning , 2010, Int. J. Robotics Res..

[68]  J. Andrew Bagnell,et al.  Efficient high dimensional maximum entropy modeling via symmetric partition functions , 2012, NIPS.

[69]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[70]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[71]  Paul Newman,et al.  Scene Signatures: Localised and Point-less Features for Localisation , 2014, Robotics: Science and Systems.

[72]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[73]  Pieter Abbeel,et al.  Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding , 2010, 2010 IEEE International Conference on Robotics and Automation.

[74]  Raquel Urtasun,et al.  Fully Connected Deep Structured Networks , 2015, ArXiv.

[75]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[76]  Yun Jiang,et al.  Hallucinated Humans as the Hidden Context for Labeling 3D Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[78]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[79]  Mathias Perrollaz,et al.  Learning-based approach for online lane change intention prediction , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[80]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

[81]  Julie Shah,et al.  Human-Robot Teaming using Shared Mental Models , 2012 .

[82]  Tarak Gandhi,et al.  Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety , 2007, IEEE Transactions on Intelligent Transportation Systems.

[83]  David J. Fleet,et al.  Dynamical binary latent variable models for 3D human pose tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[84]  Maya Cakmak,et al.  To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control , 2007, Adapt. Behav..

[85]  A. Kazi,et al.  The MORPHA style guide for icon-based programming , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[86]  Amaury Nègre,et al.  Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety , 2011, IEEE Intelligent Transportation Systems Magazine.

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

[88]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[89]  Peter K. Allen,et al.  Graspit! A versatile simulator for robotic grasping , 2004, IEEE Robotics & Automation Magazine.

[90]  Subhashini Venugopalan,et al.  Translating Videos to Natural Language Using Deep Recurrent Neural Networks , 2014, NAACL.

[91]  Alonzo Kelly,et al.  Toward Optimal Sampling in the Space of Paths , 2007, ISRR.

[92]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[93]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[94]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[95]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[96]  Stefanie Tellex,et al.  Interpreting and Executing Recipes with a Cooking Robot , 2012, ISER.

[97]  Rachid Alami,et al.  Spatial reasoning for human robot interaction , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[98]  Wolfram Burgard,et al.  Robust Visual Robot Localization Across Seasons Using Network Flows , 2014, AAAI.

[99]  Alexei A. Efros,et al.  Scene Semantics from Long-Term Observation of People , 2012, ECCV.

[100]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[101]  Emre Ugur,et al.  Goal emulation and planning in perceptual space using learned affordances , 2011, Robotics Auton. Syst..

[102]  Alan Fern,et al.  A Bayesian Approach for Policy Learning from Trajectory Preference Queries , 2012, NIPS.

[103]  David Silver,et al.  Learning to search: Functional gradient techniques for imitation learning , 2009, Auton. Robots.

[104]  Moritz Tenorth,et al.  RoboEarth - A World Wide Web for Robots , 2011, ICRA 2011.

[105]  Dieter Fox,et al.  A Spatio-Temporal Probabilistic Model for Multi-Sensor Multi-Class Object Recognition , 2007, ISRR.

[106]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[107]  Lars Petersson,et al.  Vision in and out of Vehicles , 2003, IEEE Intell. Syst..

[108]  Douglas B. Lenat,et al.  CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.

[109]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[110]  Richard Sproat,et al.  Collecting Spatial Information for Locations in a Text-to-Scene Conversion System , 2011 .

[111]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[112]  Jan Peters,et al.  Noname manuscript No. (will be inserted by the editor) Policy Search for Motor Primitives in Robotics , 2022 .

[113]  Hema Swetha Koppula,et al.  Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[114]  Trevor Darrell,et al.  Conditional Random Fields for Object Recognition , 2004, NIPS.

[115]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[116]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[117]  Fei Wang,et al.  Overtaking vehicle detection using a spatio-temporal CRF , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[118]  Siddhartha S. Srinivasa,et al.  CHOMP: Gradient optimization techniques for efficient motion planning , 2009, 2009 IEEE International Conference on Robotics and Automation.

[119]  Mohan M. Trivedi,et al.  On-road prediction of driver's intent with multimodal sensory cues , 2011, IEEE Pervasive Computing.

[120]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[121]  Alan L. Yuille,et al.  Learning Deep Structured Models , 2014, ICML.

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

[123]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[124]  J. Shotton,et al.  Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2011 .

[125]  Peter Robinson,et al.  Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[126]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[127]  Yun Jiang,et al.  Learning Object Arrangements in 3D Scenes using Human Context , 2012, ICML.

[128]  Luke Fletcher,et al.  Correlating driver gaze with the road scene for driver assistance systems , 2005, Robotics Auton. Syst..

[129]  Maya Cakmak,et al.  Keyframe-based Learning from Demonstration , 2012, Int. J. Soc. Robotics.

[130]  David A. Ferrucci,et al.  Introduction to "This is Watson" , 2012, IBM J. Res. Dev..

[131]  J. Andrew Bagnell,et al.  Perceiving, learning, and exploiting object affordances for autonomous pile manipulation , 2013, Auton. Robots.

[132]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[133]  Tamim Asfour,et al.  Synthesizing object receiving motions of humanoid robots with human motion database , 2013, 2013 IEEE International Conference on Robotics and Automation.

[134]  Siddhartha S. Srinivasa,et al.  Generating Legible Motion , 2013, Robotics: Science and Systems.

[135]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[136]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[137]  Oliver Brock,et al.  Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation , 2008, Robotics: Science and Systems.

[138]  Emilio Frazzoli,et al.  Incremental Sampling-based Algorithms for Optimal Motion Planning , 2010, Robotics: Science and Systems.

[139]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[140]  Rachid Alami,et al.  Planning human-aware motions using a sampling-based costmap planner , 2011, 2011 IEEE International Conference on Robotics and Automation.

[141]  Hema Swetha Koppula,et al.  Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation , 2013, ICML.

[142]  Maxim Likhachev,et al.  E-Graphs: Bootstrapping Planning with Experience Graphs , 2012, SOCS.

[143]  Siddhartha S. Srinivasa,et al.  Formalizing Assistive Teleoperation , 2012, Robotics: Science and Systems.

[144]  Carlos Martínez,et al.  Human-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint action , 2013, IEEE ISR 2013.

[145]  Yoshua Bengio,et al.  Hierarchical Recurrent Neural Networks for Long-Term Dependencies , 1995, NIPS.

[146]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[147]  Yoshua Bengio,et al.  An Input Output HMM Architecture , 1994, NIPS.

[148]  Dejan Pangercic,et al.  Robotic roommates making pancakes , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[149]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[150]  Matthew R. Walter,et al.  Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation , 2011, AAAI.

[151]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[152]  David J. Fleet,et al.  Topologically-constrained latent variable models , 2008, ICML '08.

[153]  Christoph Stiller,et al.  Driver intent inference at urban intersections using the intelligent driver model , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[154]  Hema Swetha Koppula,et al.  Physically Grounded Spatio-temporal Object Affordances , 2014, ECCV.

[155]  Siddhartha S. Srinivasa,et al.  Human preferences for robot-human hand-over configurations , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[156]  Harm de Vries,et al.  RMSProp and equilibrated adaptive learning rates for non-convex optimization. , 2015 .

[157]  Ruzena Bajcsy,et al.  Improved driver modeling for human-in-the-loop vehicular control , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[158]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[159]  Cees Snoek,et al.  Objects2action: Classifying and Localizing Actions without Any Video Example , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[160]  Hema Swetha Koppula,et al.  Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture , 2016, ArXiv.

[161]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[162]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[163]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[164]  Cristian Sminchisescu,et al.  Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[165]  Ruzena Bajcsy,et al.  Semiautonomous Vehicular Control Using Driver Modeling , 2014, IEEE Transactions on Intelligent Transportation Systems.

[166]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[167]  Xiaoxiao Li,et al.  Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[168]  Kevin Lee,et al.  Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions , 2014, Int. J. Robotics Res..

[169]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[170]  Anup Doshi,et al.  Lane change intent prediction for driver assistance: On-road design and evaluation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[171]  Moritz Tenorth,et al.  Understanding and executing instructions for everyday manipulation tasks from the World Wide Web , 2010, 2010 IEEE International Conference on Robotics and Automation.

[172]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[173]  Andrew McCallum,et al.  FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs , 2009, NIPS.

[174]  J. Andrew Bagnell,et al.  Maximum margin planning , 2006, ICML.

[175]  Michael S. Ryoo,et al.  Human activity prediction: Early recognition of ongoing activities from streaming videos , 2011, 2011 International Conference on Computer Vision.

[176]  Ken Perlin,et al.  Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..

[177]  Jitendra Malik,et al.  Recurrent Network Models for Human Dynamics , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[178]  Guosheng Lin,et al.  Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[179]  Anthony Stentz,et al.  Anytime RRTs , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[180]  Yong Du,et al.  Hierarchical recurrent neural network for skeleton based action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[181]  Maya Cakmak,et al.  Accelerating imitation learning through crowdsourcing , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[182]  Christoph Goller,et al.  Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[183]  Ben Taskar,et al.  Discriminative Probabilistic Models for Relational Data , 2002, UAI.

[184]  William Brendel,et al.  Learning spatiotemporal graphs of human activities , 2011, 2011 International Conference on Computer Vision.

[185]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[186]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[187]  Thorsten Joachims,et al.  Learning Socially Optimal Information Systems from Egoistic Users , 2013, ECML/PKDD.

[188]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[189]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[190]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[191]  David Silver,et al.  Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain , 2010, Int. J. Robotics Res..

[192]  Florian Schmidt,et al.  Making planned paths look more human-like in humanoid robot manipulation planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[193]  Siddhartha S. Srinivasa,et al.  A data-driven statistical framework for post-grasp manipulation , 2014, Int. J. Robotics Res..

[194]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[195]  Ross A. Knepper,et al.  Asking for Help Using Inverse Semantics , 2014, Robotics: Science and Systems.

[196]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[197]  Hema Swetha Koppula,et al.  Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[198]  Silvio Savarese,et al.  Structural-RNN: Deep Learning on Spatio-Temporal Graphs , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[199]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[200]  Jitendra Malik,et al.  Learning Rich Features from RGB-D Images for Object Detection and Segmentation , 2014, ECCV.

[201]  Rachid Alami,et al.  A Human-Aware Manipulation Planner , 2012, IEEE Transactions on Robotics.

[202]  Martial Hebert,et al.  Contextual Sequence Prediction with Application to Control Library Optimization , 2012, Robotics: Science and Systems.

[203]  Reinhard Klette,et al.  Look at the Driver, Look at the Road: No Distraction! No Accident! , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[204]  Ivan Laptev,et al.  Track to the future: Spatio-temporal video segmentation with long-range motion cues , 2011, CVPR 2011.

[205]  Geoffrey E. Hinton,et al.  Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.

[206]  Geoffrey E. Hinton,et al.  The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.

[207]  Thorsten Joachims,et al.  Learning Trajectory Preferences for Manipulators via Iterative Improvement , 2013, NIPS.

[208]  Dmitry Berenson,et al.  A robot path planning framework that learns from experience , 2012, 2012 IEEE International Conference on Robotics and Automation.

[209]  Thierry Siméon,et al.  The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty , 2007, Robotics: Science and Systems.

[210]  Li Wang,et al.  Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models , 2011, International Journal of Computer Vision.

[211]  Trevor Darrell,et al.  PANDA: Pose Aligned Networks for Deep Attribute Modeling , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[212]  Andrea Lockerd Thomaz,et al.  Generating human-like motion for robots , 2013, Int. J. Robotics Res..

[213]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[214]  Oussama Khatib,et al.  Grasping with application to an autonomous checkout robot , 2011, 2011 IEEE International Conference on Robotics and Automation.

[215]  Thorsten Joachims,et al.  Semantic Labeling of 3D Point Clouds for Indoor Scenes , 2011, NIPS.

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

[217]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[218]  Hannes Bleuler,et al.  Randomised Rough-Terrain Robot Motion Planning , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[219]  Suvrit Sra,et al.  A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x) , 2012, Comput. Stat..

[220]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[221]  Yang Wang,et al.  A dynamic conditional random field model for object segmentation in image sequences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[222]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[223]  John Langford,et al.  Search-based structured prediction , 2009, Machine Learning.

[224]  Thorsten Joachims,et al.  Contextually Guided Semantic Labeling and Search for 3D Point Clouds , 2011, ArXiv.

[225]  Wolfram Burgard,et al.  Learning Motion Patterns of People for Compliant Robot Motion , 2005, Int. J. Robotics Res..

[226]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[227]  Yisong Yue,et al.  Learning Policies for Contextual Submodular Prediction , 2013, ICML.

[228]  Sinan Kalkan,et al.  Learning Social Affordances and Using Them for Planning , 2013, CogSci.

[229]  Tomohiro Yamamura,et al.  A Driver Behavior Recognition Method Based on a Driver Model Framework , 2000 .

[230]  Ross A. Knepper,et al.  IkeaBot: An autonomous multi-robot coordinated furniture assembly system , 2013, 2013 IEEE International Conference on Robotics and Automation.

[231]  Stefanos Nikolaidis,et al.  Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[232]  Takeo Kanade,et al.  Automated Construction of Robotic Manipulation Programs , 2010 .

[233]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[234]  Marc'Aurelio Ranzato,et al.  Learning Longer Memory in Recurrent Neural Networks , 2014, ICLR.

[235]  Steven M. LaValle,et al.  Survivability: Measuring and ensuring path diversity , 2009, 2009 IEEE International Conference on Robotics and Automation.

[236]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[237]  Gerhard Weikum,et al.  YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract , 2013, IJCAI.

[238]  Jing Xiao,et al.  Efficient and effective grasping of novel objects through learning and adapting a knowledge base , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[239]  Ashutosh Saxena,et al.  Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback , 2016, ISRR.

[240]  Xinlei Chen,et al.  Mind's eye: A recurrent visual representation for image caption generation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[241]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[242]  Ashutosh Saxena,et al.  Hierarchical Semantic Labeling for Task-Relevant RGB-D Perception , 2014, Robotics: Science and Systems.

[243]  Wolfram Burgard,et al.  Learning to predict trajectories of cooperatively navigating agents , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[244]  Marcus Liwicki,et al.  Scene labeling with LSTM recurrent neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[245]  Xinlei Chen,et al.  NEIL: Extracting Visual Knowledge from Web Data , 2013, 2013 IEEE International Conference on Computer Vision.

[246]  Martial Hebert,et al.  Activity Forecasting , 2012, ECCV.

[247]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.