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This video demonstrates how our fully-learned local planner can navigate complex environments by recognizing different terrains and their traversability.
Our paper introduces a novel planner design that combines depth and semantic information. It employs the imperative learning paradigm for optimizing the planner weights end-to-end based on the planning task objective. The optimization uses a differentiable formulation of a semantic costmap, enabling the planner to differentiate between different terrains and accurately identify obstacles.
Trained entirely in simulation, ViPlanner can be applied to real-world scenes in a zero-shot manner. Moreover, our experimental results demonstrate resistance to noise and a significant decrease in terms of traversability costs compared to purely geometric approaches.
For more information:
- Visit our Project Website at leggedrobotics...
- Read our Paper arxiv.org/abs/...
- Checkout our Code github.com/leg...