Challenges in Grounding Language in the Real World
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Abstract (arXiv)
A long-term goal of Artificial Intelligence is to build a language understanding system that allows a human to collaborate with a physical robot using language that is natural to the human. In this paper we highlight some of the challenges in doing this, and propose a solution that integrates the abilities of a cognitive agent capable of interactive task learning in a physical robot with the linguistic abilities of a large language model. We also point the way to an initial implementation of this approach.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 22 references that are in the map. First posted on arXiv 20 Jun 2025.
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7 of 22 references.
References · 7
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 783
- 2018Interactive Visual Grounding of Referring Expressions for Human-Robot Interactioncited by 56
- 2024A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddingscited by 24
- 2023VAL: Interactive Task Learning with GPT Dialog Parsingcited by 14
- 2019Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoningcited by 13
- 2022Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Taskscited by 6
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