Uncertainty-driven Planner for Exploration and Navigation
Uncertainty-driven Planner for Exploration and Navigation
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DOI：
10.1109/icra46639.2022.9812423
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
G. Georgakis;Bernadette Bucher;Anton Arapin;Karl Schmeckpeper;N. Matni;Kostas Daniilidis
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文献类型：
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作者：
G. Georgakis;Bernadette Bucher;Anton Arapin;Karl Schmeckpeper;N. Matni;Kostas Daniilidis
我们认为在以前看不见的环境中，在室内场景的空间复杂性和部分可观测性构成这些任务的挑战性的探索和pointgoal导航的问题。我们认为，学习占用先验室内地图对解决这些问题提供了显着的优势。为此，我们提出了一种新的规划框架，首先学习生成超出代理视野的占用地图，其次利用模型的不确定性，为每个感兴趣的任务制定路径选择策略。对于点目标导航的政策选择路径的置信上限政策的有效和可遍历的路径，而探索的政策最大限度地提高模型的不确定性候选路径。我们使用Habitat模拟器在Matterport 3D的视觉逼真环境中进行实验，并证明：1）与竞争方法相比，探索和地图质量指标的结果有所改善，以及2）当与最先进的DD-PPO方法配对时，我们的规划模块的有效性点目标导航任务。
We consider the problems of exploration and pointgoal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging. We argue that learning occupancy priors over indoor maps provides significant advantages towards addressing these problems. To this end, we present a novel planning framework that first learns to generate occupancy maps beyond the field-of-view of the agent, and second leverages the model uncertainty over the generated areas to formulate path selection policies for each task of interest. For pointgoal navigation the policy chooses paths with an upper confidence bound policy for efficient and traversable paths, while for exploration the policy maximizes model uncertainty over candidate paths. We perform experiments in the visually realistic environments of Matterport3D using the Habitat simulator and demonstrate: 1) Improved results on exploration and map quality metrics over competitive methods, and 2) The effectiveness of our planning module when paired with the state-of-the-art DD-PPO method for the point-goal navigation task.