Learning hierarchical relationships for object-goal navigation
This addresses the problem of object-goal navigation for robots or agents in indoor environments by improving generalization and efficiency, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the challenge of efficiently navigating to small target objects by learning hierarchical object-object relationships, achieving an 82.9% gain in success rate and 93.5% gain in success weighted by path length over state-of-the-art methods.
Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without learning about object relationships in the natural environment, and thus generalize poorly across domains. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven navigation algorithm, which considers the inherent relationship between target objects, and the more salient contextual objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show an $82.9\%$ and $93.5\%$ gain over existing state-of-the-art navigation methods in terms of the success rate (SR), and success weighted by path length (SPL), respectively. We also show that our model learns to converge much faster than other algorithms, without suffering from the well-known overfitting problem. Additional details regarding the supplementary material and code are available at https://sites.google.com/eng.ucsd.edu/mjolnir.