Robots operating in human spaces must find objects such as glasses, books, or cleaning supplies that could be on the floor, shelves, or tables. This search space is naturally 3D.
When multiple objects must be searched for, such as a cup and a mobile phone, an intuitive strategy is to first hypothesize likely search regions for each target object based on semantic knowledge or past experience, then search carefully within those regions by moving the robot’s camera around the 3D environment. To be successful, it is essential for the robot to produce an efficient search policy within a designated search region under limited field of view (FOV), where target objects could be partially or completely blocked by other objects. In this work, we consider the problem setting where a robot must search for multiple objects in a search region by actively moving its camera, with as few steps as possible.
Searching for objects in a large search region requires acting over long horizons under various sources of uncertainty in a partially observable environment. For this reason, previous works have used Partially Observable Markov Decision Process (POMDP) as a principled decision-theoretic framework for object search. However, to ensure the POMDP is manageable to solve, previous works reduce the search space or robot mobility to 2D, although objects exist in rich 3D environments. The key challenges lie in the intractability of maintaining exact belief due to large state space, and the high branching factor for planning due to large observation space.
In this paper, we present a POMDP formulation for multi-object search in a 3D region with a frustum-shaped field-of-view. To efficiently solve this POMDP, we propose a multi-resolution planning algorithm based on online Monte-Carlo tree search. In this approach, we design a novel octree-based belief representation to capture uncertainty of the target objects at different resolution levels, then derive abstract POMDPs at lower resolutions with dramatically smaller state and observation spaces.
Evaluation in a simulated 3D domain shows that our approach finds objects more efficiently and successfully compared to a set of baselines without resolution hierarchy in larger instances under the same computational requirement.
Finally, we demonstrate our approach on a torso-actuated mobile robot in a lab environment. The robot finds 3 out of 6 objects placed at different heights in two 10m2 x 2m2 regions in around 15 minutes.
This demonstrates that such challenging POMDPs can be solved online efficiently and scalably with practicality for a real robot by extending existing general POMDP solvers with domain-specific structure and belief representation. This paper won RoboCup Best Paper Award!