mlsanityData to 14 Oct 2026

Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results

arXiv 2212.06759 ↗First posted 13 Dec 2022cs.RO cs.AI cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 69 of its 93 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

69 of 93 references.

References · 69

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38 more
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Citing papers per year: 2023: 4; 2024: 5; 2025: 4.

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  1. 2023ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planningcited by 365
  2. 2024SpatialBot: Precise Spatial Understanding with Vision Language Modelscited by 152
  3. 2024Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Modelscited by 32
  4. 2023Learning to See Physical Properties with Active Sensing Motor Policiescited by 10
  5. 2023One-4-All: Neural Potential Fields for Embodied Navigationcited by 7
  6. 2025TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goalscited by 7
  7. 2025ObjectReact: Learning Object-Relative Control for Visual Navigationcited by 5
  8. 2024Learning Dynamic Cognitive Map with Autonomous Navigationcited by 3
  9. 2024OpenBot-Fleet: A System for Collective Learning with Real Robotscited by 1
  10. 2025Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigationcited by 1
  11. 2023Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Uglycited by 0
  12. 2024Language, Environment, and Robotic Navigationcited by 0
1 more
  1. 2025Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learningcited by 0