Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration

We introduce a new variant of contextual multi-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features and thus provide...

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Veröffentlicht in:	IEEE robotics and automation letters 2024-10, Vol.9 (10), p.8531-8538
Hauptverfasser:	Wakayama, Shohei, Ahmed, Nisar
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bayes methods Bayesian analysis Decision making Error analysis Error correction Free energy human-robot collaboration Multi-armed bandit problems Optimization Parameter robustness Parameter uncertainty Probabilistic inference Probabilistic logic Probabilistic models Robot sensing systems Robustness Semantics Statistical inference Uncertainty
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