Multimodal estimation and communication of latent semantic knowledge for robust execution of robot instructions
暂无分享,去创建一个
Subhro Roy | Rohan Paul | Daehyung Park | Jacob Arkin | Nicholas Roy | Matthew R Walter | Thomas M Howard
[1] Hokeun Kim,et al. Multimodal anomaly detection for assistive robots , 2019, Auton. Robots.
[2] Mohit Shridhar,et al. Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction , 2018, Robotics: Science and Systems.
[3] Ivana Kruijff-Korbayová,et al. Situated resolution and generation of spatial referring expressions for robotic assistants , 2009, IJCAI 2009.
[4] Regina A. Pomranky,et al. The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..
[5] Gregory Shakhnarovich,et al. Comprehension-Guided Referring Expressions , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Stefanie Tellex,et al. Clarifying commands with information-theoretic human-robot dialog , 2013, HRI 2013.
[7] Nicholas Roy,et al. Real-Time Human-Robot Communication for Manipulation Tasks in Partially Observed Environments , 2018, ISER.
[8] Luke S. Zettlemoyer,et al. Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions , 2014, AAAI.
[9] Luke Fletcher,et al. A Situationally Aware Voice‐commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments , 2015, J. Field Robotics.
[10] Matthew R. Walter,et al. Efficient Natural Language Interfaces for Assistive Robots , 2014, IROS 2014.
[11] Dimitra Gkatzia,et al. Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments , 2015, ENLG.
[12] Devi Parikh,et al. Attributes for Classifier Feedback , 2012, ECCV.
[13] Dan Klein,et al. Learning Semantic Correspondences with Less Supervision , 2009, ACL.
[14] Li Guo,et al. Knowledge Base Completion Using Embeddings and Rules , 2015, IJCAI.
[15] Matthew R. Walter,et al. A Multiview Approach to Learning Articulated Motion Models , 2017, ISRR.
[16] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[17] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[18] Yu Zhang,et al. Temporal Spatial Inverse Semantics for Robots Communicating with Humans , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[19] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[20] Wolfram Burgard,et al. Tactile Sensing for Mobile Manipulation , 2011, IEEE Transactions on Robotics.
[21] Raymond J. Mooney,et al. Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision , 2010, COLING.
[22] Jayant Krishnamurthy,et al. Toward Interactive Grounded Language Acqusition , 2013, Robotics: Science and Systems.
[23] Alan L. Yuille,et al. Generation and Comprehension of Unambiguous Object Descriptions , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Dieter Fox,et al. Following directions using statistical machine translation , 2010, 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
[25] James M. Rehg,et al. Rapid categorization of object properties from incidental contact with a tactile sensing robot arm , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).
[26] Nicholas Roy,et al. Learning Unknown Groundings for Natural Language Interaction with Mobile Robots , 2017, ISRR.
[27] Matthew R. Walter,et al. Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation , 2016, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.
[28] Mirella Lapata,et al. Unsupervised Concept-to-text Generation with Hypergraphs , 2012, NAACL.
[29] Danica Kragic,et al. Interactive object classification using sensorimotor contingencies , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[30] Rudolph Triebel,et al. Driven Learning for Driving: How Introspection Improves Semantic Mapping , 2016, ISRR.
[31] Yejin Choi,et al. Verb Physics: Relative Physical Knowledge of Actions and Objects , 2017, ACL.
[32] Nicholas Roy,et al. Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms , 2018, Int. J. Robotics Res..
[33] Dan Klein,et al. A Simple Domain-Independent Probabilistic Approach to Generation , 2010, EMNLP.
[34] Qi Wu,et al. Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Joyce Yue Chai,et al. Embodied Collaborative Referring Expression Generation in Situated Human-Robot Interaction , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
[36] Rudolph Triebel,et al. Knowing when we don't know: Introspective classification for mission-critical decision making , 2013, 2013 IEEE International Conference on Robotics and Automation.
[37] Nicholas Roy,et al. Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context , 2017, IJCAI.
[38] Kevin Lee,et al. Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions , 2014, Int. J. Robotics Res..
[39] Matthew R. Walter,et al. Approaching the Symbol Grounding Problem with Probabilistic Graphical Models , 2011, AI Mag..
[40] Jacob Arkin,et al. Experiments in Proactive Symbol Grounding for Efficient Physically Situated Human-Robot Dialogue , 2018 .
[41] Matthew R. Walter,et al. Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation , 2011, AAAI.
[42] Matthew R. Walter,et al. Information-theoretic dialog to improve spatial-semantic representations , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[43] Felix Duvallet,et al. Imitation learning for natural language direction following through unknown environments , 2013, 2013 IEEE International Conference on Robotics and Automation.
[44] Luke S. Zettlemoyer,et al. A Joint Model of Language and Perception for Grounded Attribute Learning , 2012, ICML.
[45] Manuela M. Veloso,et al. Learning environmental knowledge from task-based human-robot dialog , 2013, 2013 IEEE International Conference on Robotics and Automation.
[46] Matthew R. Walter,et al. Learning models for following natural language directions in unknown environments , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[47] Stefanie Tellex,et al. Toward understanding natural language directions , 2010, 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
[48] Joyce Yue Chai,et al. Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication , 2017, ACL.
[49] Raymond J. Mooney,et al. Learning to sportscast: a test of grounded language acquisition , 2008, ICML '08.
[50] Stefanie Tellex,et al. A natural language planner interface for mobile manipulators , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[51] Jivko Sinapov,et al. From Acoustic Object Recognition to Object Categorization by a Humanoid Robot , 2009 .
[52] James F. Allen,et al. SALL-E: Situated Agent for Language Learning , 2013, AAAI.
[53] Trevor Darrell,et al. Robotic learning of haptic adjectives through physical interaction , 2015, Robotics Auton. Syst..
[54] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[55] Vicente Ordonez,et al. ReferItGame: Referring to Objects in Photographs of Natural Scenes , 2014, EMNLP.
[56] Jean Oh,et al. Inferring Maps and Behaviors from Natural Language Instructions , 2015, ISER.
[57] Licheng Yu,et al. Modeling Context in Referring Expressions , 2016, ECCV.
[58] Stefanie Tellex,et al. Toward Information Theoretic Human-Robot Dialog , 2012, Robotics: Science and Systems.
[59] Rudolph Triebel,et al. Introspective classification for robot perception , 2016, Int. J. Robotics Res..
[60] John D. Lafferty,et al. Correlated Topic Models , 2005, NIPS.
[61] Ning Wang,et al. Trust calibration within a human-robot team: Comparing automatically generated explanations , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
[62] Sean Andrist,et al. Rhetorical robots: Making robots more effective speakers using linguistic cues of expertise , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
[63] Mirella Lapata,et al. Collective Content Selection for Concept-to-Text Generation , 2005, HLT.
[64] Yixin Chen,et al. Link Prediction Based on Graph Neural Networks , 2018, NeurIPS.
[65] GlassJames,et al. A Situationally Aware Voice-commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments , 2015 .
[66] C. Lawrence Zitnick,et al. Learning Common Sense through Visual Abstraction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[67] Peter Stone,et al. Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" , 2016, IJCAI.
[68] Nina Dethlefs,et al. Generating Adaptive Route Instructions Using Hierarchical Reinforcement Learning , 2010, Spatial Cognition.
[69] Matthew R. Walter,et al. What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment , 2015, NAACL.
[70] Ali Farhadi,et al. Stating the Obvious: Extracting Visual Common Sense Knowledge , 2016, NAACL.
[71] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[72] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[73] Peter Stone,et al. Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions , 2018, AAAI.
[74] Danqi Chen,et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.
[75] Wolfram Burgard,et al. Learning to give route directions from human demonstrations , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[76] Marilyn A. Walker,et al. SPoT: A Trainable Sentence Planner , 2001, NAACL.
[77] Matthew R. Walter,et al. Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences , 2015, AAAI.
[78] Leslie Pack Kaelbling,et al. Logical Particle Filtering , 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.
[79] Ross A. Knepper,et al. Asking for Help Using Inverse Semantics , 2014, Robotics: Science and Systems.
[80] Matthew R. Walter,et al. A framework for learning semantic maps from grounded natural language descriptions , 2014, Int. J. Robotics Res..
[81] Connor Schenck,et al. Grounding semantic categories in behavioral interactions: Experiments with 100 objects , 2014, Robotics Auton. Syst..
[82] Luke Zettlemoyer,et al. Learning to Parse Natural Language to a Robot Execution System , 2012 .
[83] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[84] Hadas Kress-Gazit,et al. Sorry Dave, I'm Afraid I Can't Do That: Explaining Unachievable Robot Tasks Using Natural Language , 2013, Robotics: Science and Systems.
[85] Stephen G Pauker,et al. Probability Distributions , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.
[86] David Whitney,et al. Interpreting multimodal referring expressions in real time , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[87] Edwin Olson,et al. DART: A particle-based method for generating easy-to-follow directions , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[88] John D. Kelleher,et al. Incremental Generation of Spatial Referring Expressions in Situated Dialog , 2006, ACL.
[89] Sebastian Thrun,et al. Probabilistic robotics , 2002, CACM.
[90] Matthew R. Walter,et al. Learning Semantic Maps from Natural Language Descriptions , 2013, Robotics: Science and Systems.
[91] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[92] Peter Stone,et al. Learning to Interpret Natural Language Commands through Human-Robot Dialog , 2015, IJCAI.
[93] Yejin Choi,et al. Event2Mind: Commonsense Inference on Events, Intents, and Reactions , 2018, ACL.
[94] Raymond J. Mooney,et al. Generation by Inverting a Semantic Parser that Uses Statistical Machine Translation , 2007, NAACL.
[95] Regina Barzilay,et al. Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization , 2004, NAACL.
[96] Martial Hebert,et al. Integrated Intelligence for Human-Robot Teams , 2016, ISER.
[97] Matthew R. Walter,et al. A multimodal interface for real-time soldier-robot teaming , 2016, SPIE Defense + Security.