Using Language to Generate State Abstractions for Long-Range Planning in Outdoor Environments

Robots that process navigation instructions in large outdoor environments will need to operate at different levels of abstraction. For example, a land-surveying aerial robot receiving the instruction "go to Boston and go through the state forest on the way" must reason about a long-range goal like "go to Boston" while also processing a finer-grained constraint like "go through the state forest." Existing approaches struggle to plan such commands because of the immense number of locations and constraints that can be expressed in language. We introduce a hierarchical representation of outdoor environments and a planning approach that dynamically compacts the robot's state space to enable tractable planning in city and state-scale environments. Our approach leverages natural abstractions in real-world map data, coupled with abstractions generated from users' instructions, to generate filtered environment views that accelerate planning while supporting a robot's ability to obey complex temporal goals and constraints at different levels of abstraction. We evaluate our approach on seven templates of LTL f formulas and in an 80 kilometer-radius environment containing over 250,000 locations downloaded from OpenStreetMap. The results show our approach enables planning in seconds or minutes in a large outdoor environment while still satisfying the task specification.