Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations

• URL: http://arxiv.org/abs/2507.19947v2
• Date: Wed, 30 Jul 2025 13:52:22 GMT
• Title: Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
• Authors: Supawich Sitdhipol, Waritwong Sukprasongdee, Ekapol Chuangsuwanich, Rina Tse,
• Abstract summary: An uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs.<n>This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features.<n> Collaborative sensing results demonstrated that the grounded likelihood successfully enabled uncertainty-aware fusion of heterogeneous human language observations and robot sensor measurements.
• Score: 4.008130792416869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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