iPlanner: Imperative Path Planning (RSS 2023)
Автор: Robotic Systems Lab: Legged Robotics at ETH Zürich
Загружено: 2023-08-12
Просмотров: 7064
Описание:
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.
Paper: https://arxiv.org/abs/2302.11434
Code: https://github.com/leggedrobotics/iPl...
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