Fuzzy Bayesian Inference for Real-Time Human-Robot Collaboration under Linguistic Uncertainty

While Pan et al. (2020) and Xu et al. (2024) blend fuzzy logic and Bayesian networks for risk, their focus is on static risk analysis. This idea extends fuzzy Bayesian inference to the dynamic, interactive domain of human-robot collaboration, where instructions and feedback are often ambiguous or linguistically imprecise. The model would fuse linguistic input (e.g., “a bit faster,” “almost there”) with sensor data, updating beliefs about human intent and environment state in real time. Incorporating modified Dempster-Shafer evidence theory would allow the model to handle conflicting or incomplete cues. This could enable robots to more naturally and robustly collaborate with humans in manufacturing, healthcare, or service environments—unlocking new frontiers in human-AI interaction.

References:

1. Improved Fuzzy Bayesian Network-Based Risk Analysis With Interval-Valued Fuzzy Sets and D–S Evidence Theory. Yue Pan, Limao Zhang, Zhiwu Li, L. Ding (2020). IEEE transactions on fuzzy systems.
2. A New Fuzzy Bayesian Inference Approach for Risk Assessments. Jintao Xu, Yang Sui, Tao Yu, Rui Ding, Tao Dai, Mengyan Zheng (2024). Symmetry.