Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration
This addresses the challenge of using unreliable external data in robotic decision-making, but it is incremental as it builds on existing contextual multi-armed bandit and active inference methods.
The paper tackles the problem of robotic search and exploration by introducing observation-augmented contextual multi-armed bandits (OA-CMABs) that incorporate external, error-prone observations, and it shows that efficient decision-making and robust parameter inference are achieved in simulations under various conditions.
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 evidence on top of observed option outcomes to infer hidden parameters. However, if external data is error-prone, measures must be taken to preserve the correctness of inference. To this end, we derive a robust Bayesian inference process for OA-CMABs based on recently developed probabilistic semantic data association techniques, which handle complex mixture model parameter priors and hybrid discrete-continuous observation likelihoods for semantic external data sources. To cope with combined uncertainties in OA-CMABs, we also derive a new active inference algorithm for optimal option selection based on approximate expected free energy minimization. This generalizes prior work on CMAB active inference by accounting for faulty observations and non-Gaussian distributions. Results for a simulated deep space search site selection problem show that, even if incorrect semantic observations are provided externally, e.g. by scientists, efficient decision-making and robust parameter inference are still achieved in a wide variety of conditions.