Predicting and Sharing of Multi-Human Intentions in Multi-Robot Systems
A Bayesian Approach
Bibliographic Data
| ID | 17812191 |
|---|---|
| Authors | Elise Verhees (0000-0002-0802-271X, Eindhoven University of Technology, corresponding author), Sander van der Vorst (Eindhoven University of Technology), Jos Elfring (Eindhoven University of Technology), Michel Reniers (Eindhoven University of Technology), Rene van de Molengraft (Eindhoven University of Technology), Elena Torta (0000-0001-9198-1374, Eindhoven University of Technology) |
| Year | 2026 |
| Volume | 18 |
| Issue | 3 |
| Publication date | 2026-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science+Business Media (PUBLISHER • DE) |
| DOI | 10.1007/s12369-026-01382-2 |
| OpenAlex | W7133542051 |
| Language | EN |
| References cited | 44 |
This research addresses shared intention prediction in multi-human, multi-robot environments. We propose an intention prediction pipeline based on Bayesian inference, enabling robots to predict humans’ navigational intent. Our pipeline uses prior semantic knowledge about potential goal destinations of the humans. We furthermore investigated different strategies for robots to share information to improve their predictions. A dataset was collected specifically for testing our pipeline and comparing different sharing strategies. The dataset consists of camera feeds from two robots observing two humans performing simple pick-up tasks. The pipeline correctly predicts 63% of the cases. Implementing a relatively simple data sharing strategy increased accuracy to 77%. However, a poorly designed data sharing strategy reduced accuracy to 47%. A validation dataset was collected in a more realistic office setting, which supports the observed relative differences in performance, despite the increased complexity of this environment. Our key finding is thus that the choice of sharing strategy can significantly impact prediction performance. The insights gained from our experiments can help progress the development of more effective shared intention prediction in multi-human, multi-robot teams
Bayesian network · Bayesian probability · Key (lock) · Pipeline (software) · Robot · Robotics · Autonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety · Social Robot Interaction and HRI
| Highly cited | No |
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