感知至关重要：通过不确定性感知语义分割增强具身智能
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation
AI总结：
针对具身智能物体搜索任务中感知模型过时、忽略时序聚合、感知过度自信的问题，提出通过校准聚合与目标判定中的感知概率和不确定性适配序列任务，可零额外训练成本集成到现有预训练搜索模型中，实验验证了其有效性。
摘要：
具身智能（Embodied AI）在未知环境中的行动能力已取得显著进展，但物体搜索等任务大多聚焦于高效策略学习。本研究发现当前搜索方法存在若干不足：它们大多依赖过时的感知模型，忽略时序聚合，且测试时直接从真实标注（ground truth）切换到带噪声的感知输入，未考虑由此导致的感知状态过度自信问题。我们通过校准聚合过程和目标判定中的感知概率与不确定性来解决上述问题，从而使模型适配序列任务。所提方法可直接集成到现有各类搜索方法的预训练模型中，无需额外训练成本。我们在不同语义感知模型和策略下对聚合方法开展了大量评估，证实了校准后的不确定性在聚合和目标判定中均具有重要作用。代码与训练好的模型可在https://semantic-search.cs.uni-freiburg.de获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
