Memory Proxy Maps for Visual Navigation
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| Опубликовано в:: | arXiv.org (Dec 12, 2024), p. n/a |
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| Главный автор: | |
| Другие авторы: | , , , |
| Опубликовано: |
Cornell University Library, arXiv.org
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3129864249 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3129864249 | ||
| 045 | 0 | |b d20241212 | |
| 100 | 1 | |a Johnson, Faith | |
| 245 | 1 | |a Memory Proxy Maps for Visual Navigation | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 12, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network (WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task. | |
| 653 | |a Visual tasks | ||
| 653 | |a Navigation | ||
| 653 | |a Memory tasks | ||
| 653 | |a Waypoints | ||
| 653 | |a Maps | ||
| 653 | |a Visual observation | ||
| 653 | |a Obstacle avoidance | ||
| 700 | 1 | |a Cao, Bryan Bo | |
| 700 | 1 | |a Ashwin Ashok | |
| 700 | 1 | |a Jain, Shubham | |
| 700 | 1 | |a Dana, Kristin | |
| 773 | 0 | |t arXiv.org |g (Dec 12, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3129864249/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2411.09893 |