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Language, Environment, and Robotic Navigation

arXiv 2404.03049 ↗First posted 3 Apr 2024cs.CLcited by 0

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Abstract (arXiv)

This paper explores the integration of linguistic inputs within robotic navigation systems, drawing upon the symbol interdependency hypothesis to bridge the divide between symbolic and embodied cognition. It examines previous work incorporating language and semantics into Neural Network (NN) and Simultaneous Localization and Mapping (SLAM) approaches, highlighting how these integrations have advanced the field. By contrasting abstract symbol manipulation with sensory-motor grounding, we propose a unified framework where language functions both as an abstract communicative system and as a grounded representation of perceptual experiences. Our review of cognitive models of distributional semantics and their application to autonomous agents underscores the transformative potential of language-integrated systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 3 Apr 2024.

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References · 13

  1. 2022Visual Language Maps for Robot Navigationcited by 362
  2. 2022Interactive Language: Talking to Robots in Real Timecited by 217
  3. 2021Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotationcited by 117
  4. 2022Navigating to Objects in the Real Worldcited by 104
  5. 2017Grounded Language Learning in a Simulated 3D Worldcited by 100
  6. 2020PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewardscited by 30
  7. 2023Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Datacited by 20
  8. 2022Meta-Reinforcement Learning via Language Instructionscited by 18
  9. 2022Zipfian environments for Reinforcement Learningcited by 13
  10. 2022Learning Robotic Navigation from Experience: Principles, Methods, and Recent Resultscited by 13
  11. 2023Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulationscited by 6
  12. 2020Reconstructing Maps from Textcited by 2
1 more
  1. 2021Semantic Navigation Using Building Information on Construction Sitescited by 2

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