mlsanityData to 14 Oct 2026

UAS Visual Navigation in Large and Unseen Environments via a Meta Agent

arXiv 2503.15781 ↗First posted 20 Mar 2025cs.RO cs.CVcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

The aim of this work is to develop an approach that enables Unmanned Aerial System (UAS) to efficiently learn to navigate in large-scale urban environments and transfer their acquired expertise to novel environments. To achieve this, we propose a meta-curriculum training scheme. First, meta-training allows the agent to learn a master policy to generalize across tasks. The resulting model is then fine-tuned on the downstream tasks. We organize the training curriculum in a hierarchical manner such that the agent is guided from coarse to fine towards the target task. In addition, we introduce Incremental Self-Adaptive Reinforcement learning (ISAR), an algorithm that combines the ideas of incremental learning and meta-reinforcement learning (MRL). In contrast to traditional reinforcement learning (RL), which focuses on acquiring a policy for a specific task, MRL aims to learn a policy with fast transfer ability to novel tasks. However, the MRL training process is time consuming, whereas our proposed ISAR algorithm achieves faster convergence than the conventional MRL algorithm. We evaluate the proposed methodologies in simulated environments and demonstrate that using this training philosophy in conjunction with the ISAR algorithm significantly improves the convergence speed for navigation in large-scale cities and the adaptation proficiency in novel environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 28 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

21 of 28 references.

References · 21

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  7. 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
  8. 2020A Recurrent Vision-and-Language BERT for Navigationcited by 302
  9. 2016Learning to Navigate in Complex Environmentscited by 198
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  11. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 148
  12. 2017Meta Learning Shared Hierarchiescited by 93
9 more
  1. 2018Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learningcited by 93
  2. 2019Meta-Learning without Memorizationcited by 92
  3. 2018Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigationcited by 84
  4. 2019Bayesian Relational Memory for Semantic Visual Navigationcited by 48
  5. 2021Visual Navigation with Spatial Attentioncited by 43
  6. 2019Situational Fusion of Visual Representation for Visual Navigationcited by 28
  7. 2020Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learningcited by 14
  8. 2020A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learningcited by 4
  9. 2019Graph Attention Memory for Visual Navigationcited by 3

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