In this paper, we present an end-to-end planning framework based on a novel imperative learning (IL) approach. The method involves a bi-level optimization (BLO) process that combines network update and metric-based trajectory optimization during training to produce smooth and collision-free trajectories using only a single depth measurement. The IL is able to utilize task-level loss and optimize through direct gradient descent. This allows the method to be trained in an efficient unsupervised manner, eliminating the need for explicit trajectory labels.
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5 months ago 00:03:00 1
ViPlanner: Visual Semantic Imperative Learning for Local Navigation (ICRA 2024)