Grounded Language Learning: Where Robotics and NLP Meet
By Cynthia Matuszek. 2018
73 citations recorded by the source corpus.
Grounded language acquisition is concerned with learning the meaning of language as it applies to the physical world. As robots become more capable and ubiquitous, there is an increasing need for non-specialists to interact with and control them, and natural language is an intuitive, flexible, and customizable mechanism for such communication. At the same time, physically embodied agents offer a way to learn to understand natural language in the context of the world to which it refers. This paper gives an overview of the research area, selected recent advances, and some future directions and challenges that remain.
Subfields: Artificial Intelligence, Cultural Studies, Natural Language Processing, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Mohit Bansal
- Jianfeng Gao
- Luke S. Zettlemoyer
- Percy Liang
- Hannaneh Hajishirzi
- Noah A. Smith
- Graham Neubig
In Cultural Studies
- Jacob Andreas
- Yonatan Bisk
- Ari Holtzman
- Percy Liang
- Yejin Choi
- Mirella Lapata
- Yoshua Bengio
- Jesse Thomason
In Natural Language Processing
- Yejin Choi
- Mohit Bansal
- Jianfeng Gao
- Luke S. Zettlemoyer
- Graham Neubig
- Percy Liang
- Richard Socher
- Hannaneh Hajishirzi
In Reinforcement Learning
- Siddharth Karamcheti
- Yonatan Bisk
- Jesse Thomason
- Stefan Lee
- Percy Liang
- Megha Srivastava
- Jacob Andreas
- Yejin Choi
Citations in the corpus
- Experience Grounds Language (2020, background): “world (Tellex et al., 2011; Matuszek, 2018; Thomason et al., 2020; Tellex et al., 2020) must translate from language to action.”
- PIQA: Reasoning about Physical Commonsense in Natural Language (2019, background): “Language tools should provide strong initial priors for learning (Tellex et al. 2011; Matuszek 2018) that are then refined through interaction and dialogue (Gao et al. 2016).”
- LILA: Language-Informed Latent Actions (2021, background): “We additionally build off of work at the intersection of language and robotics [19, 20, 21].”
- Language-Conditioned Imitation Learning for Robot Manipulation Tasks (2020, background): “A survey of natural language for robotic task specification can be found in Matuszek [24].”
- Transferable Representation Learning in Vision-and-Language Navigation (2019, background): “These challenging settings bring simulation-based VLN work closer to real-world, language-based interaction with robots [28].”