# Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing

arXiv: https://arxiv.org/abs/2110.09621 · HTML: https://mlsanity.com/paper/2110.09621/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2209.09185/) (2022)
- [Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems](https://mlsanity.com/paper/2410.10653/) (2024)
- [Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration](https://mlsanity.com/paper/2312.12583/) (2023)
- [Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations](https://mlsanity.com/paper/2507.19947/) (2025)

## Abstract (arXiv)

Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings, unlike previous work on semantic data fusion which developed heuristic techniques for specific settings. PSDA is further incorporated into a recursive hybrid Bayesian data fusion scheme which uses Gaussian mixture priors for object states and softmax functions for semantic human sensor data likelihoods. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions where semantic human sensor data can be erroneous or contain significant reference ambiguities.