Robustness of Utilizing Feedback in Embodied Visual Navigation
Not on the map. Why?
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
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,027
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
- 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 261
- 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 244
- 2019Truly Proximal Policy Optimizationcited by 69
- 2019Just Ask:An Interactive Learning Framework for Vision and Language Navigationcited by 53
Built on by
Citing papers per year: 2026: 1.
1 paper.