Multi-Resolution POMDP Planning for Multi-Object Search in 3D

Robots operating in household environments must find objects on shelves, under tables, and in cupboards. Previous work often formulates the object search problem as a POMDP Partially Observable Markov Decision Process), yet constrain the search space in 2D to reduce computational complexity, although objects exist in a rich 3D environment. We present a POMDP formulation for multi-object search in a 3D region with a frustum-shaped field-of-view and an efficient multi-resolution planning algorithm to solve this POMDP. To achieve efficient planning, our algorithm uses a new octree-based representation that captures beliefs at different resolution levels, enabling the agent to induce abstract POMDPs with dramatically smaller state and observation spaces. Our evaluation in a simulated 3D domain shows that our approach achieves significantly higher reward ($\geq$ 51% in the largest instance) and finds more objects compared to baselines without a resolution hierarchy, as the search space becomes larger, and as the sensor uncertainty increases. We show that our approach enables a mobile robot to automatically find objects placed at different heights in two 10m$^2\times$2m regions by moving its base and actuating its torso.