Abstract: Autonomous robot exploration and object search in unknown environments are fundamental capabilities in robotics, with applications ranging from search and rescue to structural inspection. A central challenge in both tasks is that robots often must make decisions based on information they have not yet directly observed–reasoning about unexplored space, predicting future information gain, or inferring where a target object is likely to be found. This thesis argues that autonomous robots can achieve efficient exploration and long-horizon object search by leveraging spatial and semantic representations of increasing expressiveness that enable reasoning beyond immediate sensor observations.

We develop this argument through a progression of representations. We begin by extracting geometric cues–such as rooms, doorways, and spatial connectivity–directly from sensor data, and show how a team of robots can use these compact structural representations to coordinate room-based exploration in indoor environments. We then move beyond what has already been observed: by learning to predict unobserved portions of an environment, a robot can estimate the information it would gain from exploring a candidate location or path before actually visiting it, and a team of robots can use these predictions to coordinate exploration and manage information sharing under communication constraints. Finally, we extend reasoning beyond the geometric altogether, introducing a persistent 3D semantic representation that grounds open-vocabulary vision-language understanding into a structure capable of perceiving objects well beyond the range of onboard depth sensors. This representation enables a single robot to perform long-horizon object search in large outdoor environments, and allows a team of robots to coordinate search behavior by sharing a sparse, communication-efficient form of this representation rather than dense maps.

We demonstrate this progression through six contributions spanning indoor and outdoor environments and single- and multi-robot deployments, showing that as representations grow richer–from geometric structure, to predicted space, to persistent semantic memory–robots become capable of more efficient exploration, more effective object search, and more naturally coordinated teamwork.

Thesis Committee:

Sebastian Scherer (Chair)

Yonatan Bisk

Wenshan Wang

Graeme Best (University of Technology Sydney)

Micah Corah (Colorado School of Mines)