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Initial ARM and C2F-ARM.
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‎.gitignore‎

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__pycache__
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venv
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.idea
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.bash_history
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.cache/
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.local/
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nvidia-persistenced/
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results/
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rlight.egg-info

‎LICENSE‎

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Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation
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‎README.md‎

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# Attention-driven Robotic Manipulation (ARM)
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Codebase of Q-attention (within the ARM system) and coarse-to-fine Q-attention (within C2F-ARM system) from the following papers:
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- Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation (ARM system)
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- Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation (C2F-ARM system)
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![task grid image missing](readme_files/arm_c2farm.png)
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Not that C2F-ARM is the better performing system.
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## Installation
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ARM is trained using the **YARR framework**. Head to the [YARR github](https://github.com/stepjam/YARR) page and follow
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installation instructions.
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ARM is evaluated on **RLBench** 1.1.0. Head to the [RLBench github](https://github.com/stepjam/RLBench) page and follow
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installation instructions.
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Now install project requirements:
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```bash
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pip install -r requirements.txt
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```
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## Running experiments
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Be sure to have RLBench demos saved on your machine before proceeding. To generate demos for a task, go to the
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tools directory in RLBench _(rlbench/tools)_, and run:
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```bash
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python dataset_generator.py --save_path=/mnt/my/save/dir --tasks=take_lid_off_saucepan --image_size=128,128 \
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--renderer=opengl --episodes_per_task=100 --variations=1 --processes=1
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```
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Experiments are launched via [Hydra](https://hydra.cc/). To start training C2F-ARM on the
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**take_lid_off_saucepan** task with the default parameters on **gpu 0**:
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```bash
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python launch.py method=C2FARM rlbench.task=take_lid_off_saucepan rlbench.demo_path=/mnt/my/save/dir framework.gpu=0
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```

‎arm/__init__.py‎

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‎arm/arm/__init__.py‎

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import arm.arm.launch_utils

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