Probabilistic active filtering with gaussian processes for occluded object search in clutter

This paper proposes a Gaussian process model-based probabilistic active learning approach for occluded object search in clutter. Due to heavy occlusions, an agent must be able to gradually reduce uncertainty during the observations of objects in its workspace by systematically rearranging them. In this work, we apply a Gaussian process to capture the uncertainties of both system dynamics and observation function. Robot manipulation is optimized by mutual information that naturally indicates the potential of moving one object to search for new objects based on the predicted uncertainties of two models. An active learning framework updates the state belief based on sensor observations. We validated our proposed method in a simulation robot task. The results demonstrate that with samples generated by random actions, the proposed method can learn intelligent object search behaviors while iteratively converging its predicted state to the ground truth.