mlsanityData to 14 Oct 2026

Robustness of Utilizing Feedback in Embodied Visual Navigation

arXiv 2303.15453 ↗First posted 6 Mar 2023cs.LG cs.HC cs.ROcited by 1

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Abstract (arXiv)

This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its field of view. To make the agent more robust in scenarios where a teacher may not always be available, the proposed training curriculum includes a mix of episodes with and without feedback. The results show that this approach improves the agent's performance, even in the absence of feedback.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

7 of 9 references.

References · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,027
  3. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
  4. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 261
  5. 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 244
  6. 2019Truly Proximal Policy Optimizationcited by 69
  7. 2019Just Ask:An Interactive Learning Framework for Vision and Language Navigationcited by 53

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