用常识引导探索路径，让机器人更聪明地找东西
IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation
- 结合语义分割与贝叶斯滤波，动态更新目标物体概率
- 利用大模型常识先验，规划高效探索路径，SPL超前20%以上
- 零样本部署，实机验证有效，适合真实场景机器人导航
在未知环境中高效导航至目标物体是通用智能机器人的关键能力。现有方法通常采用模块化策略，融合经典探索算法（如前沿探索）与学习型语义映射模块。本文提出一种新的信息性路径规划与3D物体概率映射方法：映射模块通过语义分割和贝叶斯滤波计算目标物体出现概率，并存储常见物体的概率分布，利用大语言模型提供的常识先验指导探索。规划器在当前视角捕获足够高置信度的物体体素时终止。尽管采用零样本策略，该方法在Habitat ObjectNav Challenge 2023中以成功加权路径长度（SPL）和软SPL指标达到领先水平，性能超越其他方法超过20%。此外，已在真实机器人上验证其有效性。
原文摘要 · Abstract (English)
Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation problem have embraced a modular strategy, integrating classical exploration algorithms-notably frontier exploration-with a learned semantic mapping/exploration module. This paper introduces a novel informative path planning and 3D object probability mapping approach. The mapping module computes the probability of the object of interest through semantic segmentation and a Bayes filter. Additionally, it stores probabilities for common objects, which semantically guides the exploration based on common sense priors from a large language model. The planner terminates when the current viewpoint captures enough voxels identified with high confidence as the object of interest. Although our planner follows a zero-shot approach, it achieves state-of-the-art performance as measured by the Success weighted by Path Length (SPL) and Soft SPL in the Habitat ObjectNav Challenge 2023, outperforming other works by more than 20%. Furthermore, we validate its effectiveness on real robots. Project webpage: https://ippon-paper.github.io/
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