论文标题
学习在连续动作空间中学习多个对象搜索的长跑机器人勘探策略
Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces
论文作者
论文摘要
基于视觉的导航和探索的最新进展显示出在室内环境中令人印象深刻的能力。但是，这些方法仍然在长途任务上遇到困难，并且需要大量数据以推广到看不见的环境。在这项工作中，我们提出了一种新型的增强学习方法，用于多对象搜索，该方法将短期和长期推理结合在单个模型中，同时避免了层次结构产生的复杂性。与在粒状离散作用空间中起作用的现有多对象搜索方法相反，我们的方法在连续的动作空间中实现了出色的性能。我们进行了广泛的实验，并表明它可以概括地看不见数据有限的公寓环境。此外，我们证明了在现实世界实验中，学识渊博的政策向办公室环境的零射击转移。
Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexities arising from hierarchical structures. In contrast to existing multi-object search methods that act in granular discrete action spaces, our approach achieves exceptional performance in continuous action spaces. We perform extensive experiments and show that it generalizes to unseen apartment environments with limited data. Furthermore, we demonstrate zero-shot transfer of the learned policies to an office environment in real world experiments.
