Update README.md

Added configuration and training instructions.

README.md

@@ -7,11 +7,118 @@ tags:
 
 # 2024 RoboGrasp Dataset
 
-This is dataset the dataset for the RoboGrasp Hackathon 2024. It includes 108 simulated pick-and-place robot episodes obtained through the teleoperated demonstration.
-The episodes includes three types of items:
+This is dataset the dataset for the RoboGrasp Hackathon 2024. 
+It includes 108 simulated pick-and-place robot episodes collected on a [simulated mobile aloha environment](https://github.com/HumanoidTeam/gym-aloha-hackathon.git) through teleoperated demonstrations.
+During the collected episodes the right arm of the robot is used to pick an item from the table and put it in a box on the top of the table. 
+
+There are three types of items:
 - green cube (47% of episodes)
 - red sphere (30% of episodes)
 - blue cylinder (22% of episodes)
+  
+You can visualize the dataset episodes [here](https://huggingface.co/spaces/lerobot/visualize_dataset?dataset=HumanoidTeam%2Frobograsp_hackathon_2024&episode=0) 
+
+# Hackathon Task
+
+## Clone Repository
+As a first step for the hackathon: clone the [hackathon repository](https://github.com/HumanoidTeam/lerobot-hackathon.git) on the hackathon branch on your system.
+It's a fork of the original LeRobot repository containing the assets necessary for running following tasks.
+```bash
+git clone -b hackathon https://github.com/HumanoidTeam/lerobot-hackathon.git
+```
+
+## Install Dependencies
+After cloning the repository you can proceed in installing the dependencies using poetry. you can install poetry by running `pip install poetry`.
+```bash
+cd lerobot-hackathon
+poetry lock
+poetry build
+poetry install --extras "gym-aloha"
+```
+This will create the virtual environment with all the dependencies required. You can source the environment by running `poetry shell` within the folder.
+From this point, you can configure and run your policy training using all the models present in Lerobot (e.g. ACT, DiffusionPolicy, VQ-Bet, etc...).
+
+## Policy Configuration
+You can create a yaml file within the folder `lerobot/configs/policy/`. For example `robograsp2024_submission_model.yaml`.
+
+Within the the yaml file you can configure the input/output data shapes, data normalization strategies, context length and policy parameters. 
+
+Some parts of the yaml file will be dependant on this dataset. Here are working configurations for the input and output structure:
+
+These go at the beginning of the yaml:
+```yaml
+seed: 100000
+dataset_repo_id: HumanoidTeam/robograsp_hackathon_2024
+
+override_dataset_stats:
+  observation.images.left_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.right_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [ [ [ 0.485 ] ], [ [ 0.456 ] ], [ [ 0.406 ] ] ]  # (c,1,1)
+    std: [ [ [ 0.229 ] ], [ [ 0.224 ] ], [ [ 0.225 ] ] ]  # (c,1,1)
+  observation.images.top:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+```
+
+These go within the `policy:` scope and regards the input/output datashape
+
+```yaml
+  input_shapes:
+    observation.images.left_wrist: [3, 480, 640]
+    observation.images.right_wrist: [3, 480, 640]
+    observation.images.top: [3, 480, 640]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.left_wrist: mean_std
+    observation.images.right_wrist: mean_std
+    observation.images.top: mean_std
+    observation.state: min_max
+  output_normalization_modes:
+    action: min_max
+```
+
+The remaining configuration can be derived from the other examples provided by the lerobot original repo.
+
+## Start Policy Training
+
+you can start the policy training by running the following command, while having sourced the environment built in the previous section.
+To source the environment run:
+```bash
+poetry shell
+```
+To start the training, you can use this command: 
+```bash
+ MUJOCO_GL="egl" python lerobot/scripts/train.py \                
+    policy=robograsp2024_submission_model \
+    env=humanoid_hackathon_mobile_aloha \
+    env.task=AlohaHackathon-v0 \
+    dataset_repo_id=HumanoidTeam/robograsp_hackathon_2024
+```
+Where `robograsp2024_submission_model` is the name of the yaml file with the policy configuration, `humanoid_hackathon_mobile_aloha` is the provided yaml configuration for the mujoco environment to test the trained policies.
+
+### Resume policy training from a checkpoint.
+Terminated training too early? No worries! you can resume training from a previous checkpoint by running:
+
+```bash
+ MUJOCO_GL="egl" python lerobot/scripts/train.py \                
+    policy=robograsp2024_submission_model \
+    env=humanoid_hackathon_mobile_aloha \
+    env.task=AlohaHackathon-v0 \
+    dataset_repo_id=HumanoidTeam/robograsp_hackathon_2024 \
+    hydra.run.dir=OUTPUT_PATH \
+    resume=true
+```
+Where `OUTPUT_PATH` is the path to the checkpoint folder. It should look something like `outputs/train/2024-10-23/18-38-31_aloha_MODELTYPE_default`
+
 
  
 This dataset was created using [🤗 LeRobot](https://github.com/huggingface/lerobot).