用于人机观测贝叶斯融合的空间语言似然接地网络
Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
AI总结：
针对人机协作中机器人感知局限及融合框架需不确定性感知接地似然的问题，提出FP-LGN网络，通过三阶段课程学习实现空间语言接地并捕捉不确定性，在NLL上匹配专家规则且鲁棒性更强，有效提升人机协作任务性能。
摘要：
融合人类观测信息可帮助机器人克服协作任务中的感知局限。然而，具备不确定性感知能力的融合框架需要一个能表征人类输入不确定性的接地似然。本文提出特征金字塔似然接地网络（Feature Pyramid Likelihood Grounding Network, FP-LGN），该网络通过学习相关地图图像特征及其与空间关系语义的关联，实现空间语言的接地。模型被训练为概率估计器，采用三阶段课程学习捕捉人类语言中的随机不确定性。结果显示，FP-LGN在平均负对数似然（Negative Log-Likelihood, NLL）上与专家设计的规则相当，且标准差更低，鲁棒性更强。协作感知结果表明，该接地似然成功实现了异质人类语言观测与机器人传感器测量的不确定性感知融合，显著提升了人机协作任务的性能。
英文摘要：
Fusing information from human observations can help robots overcome sensing limitations in collaborative tasks. However, an uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs. This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features and their relationships with spatial relation semantics. The model is trained as a probability estimator to capture aleatoric uncertainty in human language using three-stage curriculum learning. Results showed that FP-LGN matched expert-designed rules in mean Negative Log-Likelihood (NLL) and demonstrated greater robustness with lower standard deviation. Collaborative sensing results demonstrated that the grounded likelihood successfully enabled uncertainty-aware fusion of heterogeneous human language observations and robot sensor measurements, achieving significant improvements in human-robot collaborative task performance.
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