iPlanner: Imperative Path Planning (RSS 2023)

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: arxiv.org/abs/...
Code: github.com/leg...

[@SlavaRC]

Awesome robots, excellent work my friends! 💥💯👍🌟

[@norik1616]

Clean solution

[@yuelinzhurobotics]

Cool Solution.

[@rjung_ch]

👍💪✌

[@madansenapati6727]

software is good but hardware seems slow and heavy to move, if haedware gets agile it will be a lethal combination