MOPA: Modular Object Navigation with PointGoal Agents
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911765711814656 |
|---|---|
| author | Raychaudhuri, Sonia Campari, Tommaso Jain, Unnat Savva, Manolis Chang, Angel X. |
| author_facet | Raychaudhuri, Sonia Campari, Tommaso Jain, Unnat Savva, Manolis Chang, Angel X. |
| contents | We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI. MOPA consists of four modules: (a) an object detection module trained to identify objects from RGB images, (b) a map building module to build a semantic map of the observed objects, (c) an exploration module enabling the agent to explore the environment, and (d) a navigation module to move to identified target objects. We show that we can effectively reuse a pretrained PointGoal agent as the navigation model instead of learning to navigate from scratch, thus saving time and compute. We also compare various exploration strategies for MOPA and find that a simple uniform strategy significantly outperforms more advanced exploration methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03696 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MOPA: Modular Object Navigation with PointGoal Agents Raychaudhuri, Sonia Campari, Tommaso Jain, Unnat Savva, Manolis Chang, Angel X. Robotics Computer Vision and Pattern Recognition We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI. MOPA consists of four modules: (a) an object detection module trained to identify objects from RGB images, (b) a map building module to build a semantic map of the observed objects, (c) an exploration module enabling the agent to explore the environment, and (d) a navigation module to move to identified target objects. We show that we can effectively reuse a pretrained PointGoal agent as the navigation model instead of learning to navigate from scratch, thus saving time and compute. We also compare various exploration strategies for MOPA and find that a simple uniform strategy significantly outperforms more advanced exploration methods. |
| title | MOPA: Modular Object Navigation with PointGoal Agents |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.03696 |