スケール選好条件付けによる地形コストマップ生成
Terrain Costmap Generation via Scaled Preference Conditioning
合成データを活用して未知地形に汎化しつつ、テスト時にユーザー指定のスケール選好で相対コストを迅速に適応できる地形コストマップ生成手法SPACERを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Luisa Mao, Garrett Warnell, Peter Stone, Joydeep Biswas
分類: cs.RO
原文アブストラクト
Successful autonomous robot navigation in off-road domains requires the ability to generate high-quality terrain costmaps that are able to both generalize well over a wide variety of terrains and rapidly adapt relative costs at test time to meet mission-specific needs. Existing approaches for costmap generation allow for either rapid test-time adaptation of relative costs (e.g., semantic segmentation methods) or generalization to new terrain types (e.g., representation learning methods), but not both. In this work, we present scaled preference conditioned all-terrain costmap generation (SPACER), a novel approach for generating terrain costmaps that leverages synthetic data during training in order to generalize well to new terrains, and allows for rapid test-time adaptation of relative costs by conditioning on a user-specified scaled preference context. Using large-scale aerial maps, we provide empirical evidence that SPACER outperforms other approaches at generating costmaps for terrain navigation, with the lowest measured regret across varied preferences in five of seven environments for global path planning.
