arXiv:2606.29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over multiple rounds. To solve this problem, we propose LinMatch, an online learning algorithm that updates the confidence intervals of the unknown features and makes the optimistic matching under uncertainty. The contributions and novelty of this work are twofold. First, we recast the optimistic matching problem in each roun
The increasing sophistication of autonomous systems and the demand for more effective human-robot collaboration in complex environments are driving research into advanced teaming algorithms.
Optimizing multi-human multi-robot teaming is crucial for future applications in defense, logistics, and disaster response, where efficiency and adaptability are paramount.
The development of robust online learning algorithms like LinMatch could significantly enhance the capabilities of AI agents to dynamically adapt and optimize team performance with unknown robotic features.
- · AI/Robotics researchers
- · Defense contractors
- · Logistics and supply chain companies
- · Automation software developers
- · Legacy uncoordinated robotic systems
- · Manual oversight roles in complex operations
Improved coordination and efficiency in multi-robot systems operating alongside humans.
Expansion of autonomous team applications into more diverse and critical sectors due to enhanced reliability and adaptability.
Potential for new ethical and legal frameworks to govern autonomous teams where responsibility and decision-making are shared between humans and AI.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG