Learning to Follow Navigational Directions

By Adam Vogel, Dan Jurafsky. 2010

211 citations recorded by the source corpus.

Subfields: Natural Language Processing, Reinforcement Learning

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Citations in the corpus

Language Understanding for Text-based Games using Deep Reinforcement Learning (2015, background): “, 2010), navigating with directions (Vogel and Jurafsky, 2010; Kollar et al., 2010; Artzi and Zettlemoyer, 2013; Matuszek et al., 2013; Andreas and Klein, 2015) and playing computer games (Eisenstein et al.”
Learning to Understand Goal Specifications by Modelling Reward (2018, methodology): “In the case of navigational instructions the trajectories of the agent and the demonstrators can sometimes be compared without relying on the actions, like e.g. Vogel & Jurafsky (2010), but for other types of instructions such a hard-coded comparison may be infeasible.”
Learning to Solve Arithmetic Word Problems with Verb Categorization (2014, background): “…is also closely related to the grounded language acquisition research (Snyder and Barzi-lay, 2007; Branavan et al., 2009; Branavan et al., 2012; Vogel and Jurafsky, 2010; Chen et al., 2010; Hajishirzi et al., 2011; Chambers and Jurafsky, 2009; Liang et al., 2009; Bordes et al., 2010) where the…”
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments (2017, background): “This is typically achieved either by assuming that the set of all navigation goals, or objects to be acted upon, has been enumerated, and that each will be identified by label [11, 43], or by operating in severely restricted or simulated environments requiring limited perception [10, 16, 19, 24, 29,”
Learning to Automatically Solve Algebra Word Problems (2014, background): “Reinforcement learning can be used to learn to read instructions and perform actions in an external world (Brana-van et al., 2009; Branavan et al., 2010; Vogel and Jurafsky, 2010).”
Deep Reinforcement Learning for Dialogue Generation (2016, background): “, 2011), or understanding dialogues that give navigation directions (Vogel and Jurafsky, 2010).”
Learning Dependency-Based Compositional Semantics (2011, background): “, 2009; Goldwasser and Roth, 2011), and navigate a map by following directions (Vogel and Jurafsky, 2010; Chen and Mooney, 2011).”
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input (2014, background): “Integrated systems that execute commands : Others [9, 10, 11, 12, 13] focus on the task of learning the representation of natural language in the restricted setting of executing commands.”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, methodology): “Combinatory Categorial Grammars (CCGs) CCGs are a linguistically-motivated formalism for modeling a wide range of language phenomena (Steedman, 1996, 2000).”
Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation (2011, methodology): “Others have used generative and discriminative models for understanding route instructions, but did not use the hierarchical nature of the language to understand mobile manipulation commands (Kollar et al., 2010; Matuszek, Fox, and Koscher, 2010; Vogel and Jurafsky, 2010).”
Learning to Interpret Natural Language Navigation Instructions from Observations (2011, methodology): “Vogel and Jurafsky (2010) built a learning system for the HCRC Map Task corpus (Anderson et al. 1991) that uses reinforcement learning to learn to navigate from one landmark to another.”
Lifelong and Continual Learning Dialogue Systems: Learning during Conversation (2021, background): “For example, authors of (Vogel and Jurafsky 2010; Misra, Langford, and Artzi 2017; Tellex et al. 2020) have explored deep learning and reinforcement learning to ground NL commands directly into executable actions.”
Modular Multitask Reinforcement Learning with Policy Sketches (2016, methodology): “In all cases, our approach substantially outperforms previous approaches based on explicit decomposition of the Q function along subtasks (Parr & Russell, 1998; Vogel & Jurafsky, 2010), unsupervised option discovery (Bacon & Precup, 2015), and several standard policy gradient baselines.”
Learning Language Games through Interaction (2016, background): “, 2011, 2014), and following instructions (Vogel and Jurafsky, 2010; Chen and Mooney, 2011; Artzi and Zettlemoyer, 2013) Semantic parsing utterances to logical forms, which we leverage, plays an important role in these set-”
Learning to Win by Reading Manuals in a Monte-Carlo Framework (2011, background): “Within this line of work, we are most closely related to reinforcement learning approaches that learn language by proactively interacting with an external environment (Branavan et al., 2009; Branavan et al., 2010; Vogel and Jurafsky, 2010).”
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning (2017, background): “, 2015, 2016), alignment models (Andreas and Klein, 2015), reinforcement learning (Branavan et al., 2009, 2010; Vogel and Jurafsky, 2010), and neural network models (Mei et al.”
Vision-and-Dialog Navigation (2019, background): “n language conditioned on higher-level, visual observations of what a shortest-path planner would do next. In some ways, CVDN echoes an older human-human, spoken dialog corpus of map-based navigation [26]. While CVDN is typed, rather than spoken, it contains many more dialogs (2k versus 128) and mor”
Alignment-Based Compositional Semantics for Instruction Following (2015, background): “Linear policy estimators An alternative family of approaches is based on learning a policy over primitive actions directly (Branavan et al., 2009; Vogel and Jurafsky, 2010).”
Neural Modular Control for Embodied Question Answering (2018, background): “Recent deep reinforcement learning-based approaches to this explore it in 2D gridworlds [16, 20, 21], simple visual [22–27] and textual [28, 29] environments, perceptually-realistic 3D home simulators [1, 30–33], as well as real indoor scenes [34–36].”
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation (2019, background): “Extensive efforts have been made to ground language instructions into a sequence of actions (MacMahon et al., 2006; Branavan et al., 2009; Vogel & Jurafsky, 2010; Tellex et al., 2011; Artzi & Zettlemoyer, 2013; Andreas & Klein, 2015; Mei et al., 2016; Cohn et al., 2016; Misra et al., 2017).”