Comments (2)
srama2512
commented on July 16, 2024
Q1. You can check out the relevant lines here for how we generate GT actions for imitation. In practice, I did not find a difference b/w RL and IL training. So I just stuck to RL training.
Q2. The A* planner only gives a short-term goal (x, y) to visit. The local policy is responsible for taking actions to reach that (x, y). We have both learned and heuristic versions of the local policy here.
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srama2512
commented on July 16, 2024
Closing this after communication over email.
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Related Issues (20)
- actuation noise HOT 4
- Mapper training during policy training HOT 2
- Not able to load trained models HOT 9
- How is "done" variable being defined in exploration task? HOT 1
- What's does height_thresh means when generating map? HOT 2
- RuntimeError when running run.py for training HOT 1
- How to set appropriate values in .yaml file to avoid low GPU-uti but high GPU Memory-usage? HOT 3
- Normalization of image in Mapper HOT 2
- How to generate episode datasets for exploration task after changing the height and radius of agent? HOT 2
- No module named 'habitat_baselines.common.env_utils' HOT 3
- Custom Dataset Evaluation HOT 3
- Run OccupancyAnticipation in Colab HOT 1
- What's the behind meaning of "GT_TYPE: wall_occupancy" in the configuration .yaml file? HOT 2
- Unexpected results for pre-trained model HOT 1
- RuntimeError: inverse_cuda: For batch 0: U(163642880,163642880) is zero, singular U HOT 1
- mp3d configs HOT 2
- RuntimeError: Error(s) in loading state_dict for Mapper: HOT 1
- What does map_size mean? HOT 1
- How to use AR? HOT 3
- Question about local masks
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