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README.md
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@ -17,7 +17,7 @@ We propose **a learning-based planner for autonomous navigation in obstacle-dens
**Learning-based Planner:** Considering the multi-modal nature of the navigation problem and to avoid local minima around initial values, our approach adopts a set of motion primitives as anchor to cover the searching space, and predicts the offsets and scores of primitives for further improvement (like the one-stage object detector YOLO).
**Training Strategy:** Compared to giving expert demonstrations for imitation in imitation learning or exploring by trial-and-error in reinforcement learning, we directly back-propagate the numerical gradient (e.g. from ESDF) to the weights of neural network in the training process, which is realistic, accurate, and timely.
**Training Strategy:** Compared to giving expert demonstrations for imitation in imitation learning or exploring by trial-and-error in reinforcement learning, we directly back-propagate the numerical gradient (e.g. from ESDF) to the weights of neural network in the training process, which is straightforward, accurate, timely, and simplifies data collection.
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@ -117,7 +117,7 @@ pip install -e .
## Train the Policy
**1. Data collection**
For efficiency, we proactively collect dataset (images and states) by randomly initializing the drone's state (position and orientation). It may take nearly 1 hour for collection with default dataset size but you only need to collect once. The data will be saved at `run/yopo_sim`.
For efficiency, we proactively collect dataset (images, states, and map) by randomly initializing the drone's states (positions and orientations). It may take nearly 1 hour for collection with default dataset size but you only need to collect once. The data will be saved at `run/yopo_sim`.
```
cd ~/YOPO/run
conda activate yopo