QuASH: 自然言語ヒューリスティクスによる視覚言語ロボットマップのクエリ
QuASH: Using Natural-Language Heuristics to Query Visual-Language Robotic Maps
視覚言語モデルの埋め込みを用いたロボットマップに対し、クエリに関連する同義語・反意語を活用して環境をマッチ/非マッチに分類するクエリ手法を提案し、マップと画像のクエリ性能を向上させた。
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著者: Matti Pekkanen, Francesco Verdoja, Ville Kyrki
分類: cs.RO
原文アブストラクト
Embeddings from Visual-Language Models are increasingly utilized to represent semantics in robotic maps, offering an open-vocabulary scene understanding that surpasses traditional, limited labels. Embeddings enable on-demand querying by comparing embedded user text prompts to map embeddings via a similarity metric. The key challenge in performing the task indicated in a query is that the robot must determine the parts of the environment relevant to the query. This paper proposes a solution to this challenge. We leverage natural-language synonyms and antonyms associated with the query within the embedding space, applying heuristics to estimate the language space relevant to the query, and use that to train a classifier to partition the environment into matches and non-matches. We evaluate our method through extensive experiments, querying both maps and standard image benchmarks. The results demonstrate increased queryability of maps and images. Our querying technique is agnostic to the representation and encoder used, and requires limited training.
