知覚が重要：不確実性を考慮したセマンティックセグメンテーションによる身体性AIの強化
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation
物体探索タスクにおいて、知覚モデルの不確実性を校正し時系列で集約することで、既存の探索手法を追加学習なしに改善する手法を提案した。
著者: Sai Prasanna, Daniel Honerkamp, Kshitij Sirohi, Tim Welschehold, Wolfram Burgard, Abhinav Valada
分類: cs.RO, cs.CV
原文アブストラクト
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, we identify several gaps in current search methods: They largely focus on dated perception models, neglect temporal aggregation, and transfer from ground truth directly to noisy perception at test time, without accounting for the resulting overconfidence in the perceived state. We address the identified problems through calibrated perception probabilities and uncertainty across aggregation and found decisions, thereby adapting the models for sequential tasks. The resulting methods can be directly integrated with pretrained models across a wide family of existing search approaches at no additional training cost. We perform extensive evaluations of aggregation methods across both different semantic perception models and policies, confirming the importance of calibrated uncertainties in both the aggregation and found decisions. We make the code and trained models available at https://semantic-search.cs.uni-freiburg.de.
