NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments
<|reference_start|>NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments: In unknown cluttered and dynamic environments such as disaster scenes, mobile robots need to perform target-driven navigation in order to find people or objects of interest, while being solely guided by images of the targets. In this paper, we introduce NavFormer, a novel end-to-end transformer architecture developed for robot target-driven navigation in unknown and dynamic environments. NavFormer leverages the strengths of both 1) transformers for sequential data processing and 2) self-supervised learning (SSL) for visual representation to reason about spatial layouts and to perform collision-avoidance in dynamic settings. The architecture uniquely combines dual-visual encoders consisting of a static encoder for extracting invariant environment features for spatial reasoning, and a general encoder for dynamic obstacle avoidance. The primary robot navigation task is decomposed into two sub-tasks for training: single robot exploration and multi-robot collision avoidance. We perform cross-task training to enable the transfer of learned skills to the complex primary navigation task without the need for task-specific fine-tuning. Simulated experiments demonstrate that NavFormer can effectively navigate a mobile robot in diverse unknown environments, outperforming existing state-of-the-art methods in terms of success rate and success weighted by (normalized inverse) path length. Furthermore, a comprehensive ablation study is performed to evaluate the impact of the main design choices of the structure and training of NavFormer, further validating their effectiveness in the overall system.<|reference_end|>
@article{wang2024navformer:,
    title={NavFormer: A Transformer Architecture for Robot Target-Driven Navigation
  in Unknown and Dynamic Environments},
    author={Haitong Wang, Aaron Hao Tan, Goldie Nejat},
    journal={arXiv preprint arXiv:2402.06838},
    year={2024},
    doi={10.1109/LRA.2024.3412638},
    archivePrefix={arXiv},
    eprint={2402.06838},
    primaryClass={cs.RO}
}