ROS-HPL: Robotic Object Search with Hierarchical Policy Learning and Intrinsic-Extrinsic Modeling

Despite significant progress in Robotic Object Search (ROS) over the recent years with deep reinforcement learning based approaches, the sparsity issue in reward setting as well as the lack of interpretability of the previous ROS approaches leave much to be desired. We present a novel policy learning approach for ROS, based on a hierarchical and interpretable modeling with intrinsic/extrinsic reward setting, to tackle these two challenges. More specifically, we train the low-level policy by deliberating between an action that achieves an immediate sub-goal and the one that is better suited for achieving the final goal. We also introduce a new evaluation metric, namely the extrinsic reward, as a harmonic measure of the object search success rate and the average steps taken. Experiments conducted with multiple settings on the House3D environment validate and show that the intelligent agent, trained with our model, can achieve a better object search performance (higher success rate with lower average steps, measured by SPL: Success weighted by inverse Path Length). In addition, we conduct studies w.r.t. the parameter that controls the weighted overall reward from intrinsic and extrinsic components. The results suggest it is critical to devise a proper trade-off strategy to perform the object search well.