Skip-SCAR: A Modular Approach to ObjectGoal Navigation with Sparsity and Adaptive Skips
CoRR(2024)
摘要
In ObjectGoal navigation (ObjectNav), agents must locate specific objects
within unseen environments, requiring effective observation, prediction, and
navigation capabilities. This study found that traditional methods looking only
for prediction accuracy often compromise on computational efficiency. To
address this, we introduce "Skip-SCAR," a modular framework that enhances
efficiency by leveraging sparsity and adaptive skips. The SparseConv-Augmented
ResNet (SCAR) at the core of our approach uses sparse and dense feature
processing in parallel, optimizing both the computation and memory footprint.
Our adaptive skip technique further reduces computational demands by
selectively bypassing unnecessary semantic segmentation steps based on
environmental constancy. Tested on the HM3D ObjectNav datasets, Skip-SCAR not
only minimizes resource use but also sets new performance benchmarks,
demonstrating a robust method for improving efficiency and accuracy in robotic
navigation tasks.
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