An energy saving approach to active object recognition and localization

We propose an Active Object Recognition (AOR) strategy explicitly suited to work with robotic arms in human-robot cooperation scenarios. So far, AOR policies on robotic arms have focused on heterogeneous constraints, most of them related to classification accuracy, classification confidence, number of moves etc., discarding physical and energetic constraints a real robot has to fulfill. Our strategy overcomes this weakness by exploiting a POMDP-based AOR algorithm that explicitly considers manipulability and energetic terms in the planning optimization. The manipulability term avoids the robotic arm to get close to singularities, which require expensive and straining backtracking steps; the energetic term deals with the arm gravity compensation when in static conditions, which is crucial in AOR policies where time is spent in the classifier belief update, before doing the next movement. Several experiments have been carried out on a redundant, 7-DoF Panda arm manipulator, on a multi-object recognition task. This allows to appreciate the improvement of our solution with respect to other competitors evaluated on simulations only.