Long-Horizon Navigation is the ability of embodied agents to efficiently explore, map, and navigate complex environments over extended temporal and spatial scales. This involves constructing a topological representation of the scene, leveraging motion planning to reach specified waypoints, and employing object navigation strategies to locate and transport semantic targets. The modular hierarchical transport policy enables generalization to more challenging environments by training on simplified tasks, ensuring adaptability. Additionally, long-horizon navigation incorporates deep exploration and planning to optimize object retrieval and transport while managing load constraints and variable capacities.

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Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport

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We aim to address key challenges in long-horizon embodied exploration and navigation by proposing a long-horizon object transport task called Long-HOT and a novel modular framework for temporally extended navigation. Agents in Long-HOT need to efficiently find and pick up target objects that are scattered in the environment, carry them to a goal location with load constraints, and optionally have access to a container. We propose a modular topological graph-based transport policy (HTP) that explores efficiently with the help of weighted frontiers. Our hierarchical approach uses a combination of motion planning algorithms to reach point goals within explored locations and object navigation policies for moving towards semantic targets at unknown locations. Experiments on both our proposed Habitat transport task and on MultiOn benchmarks show that our method outperforms baselines and prior works. Further, we analyze the agent’s behavior for the usage of the container and demonstrate meaningful generalization to harder transport scenes with training only on simpler versions of the task.

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