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README.md

@@ -1,139 +1,227 @@
 ---
+license: apache-2.0
 task_categories:
 - robotics
 tags:
 - LeRobot
-- RoboGrasp2024
-- Virtual
-pretty_name: RoboGrasp 2024 Hackathon Dataset
+configs:
+- config_name: default
+  data_files: data/*/*.parquet
 ---
 
-# 2024 RoboGrasp Dataset
-
-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. Thus, we provide the parameters necessary to use 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
+This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
+
+## Dataset Description
+
+
+
+- **Homepage:** [More Information Needed]
+- **Paper:** [More Information Needed]
+- **License:** apache-2.0
+
+## Dataset Structure
+
+[meta/info.json](meta/info.json):
+```json
+{
+    "codebase_version": "v2.0",
+    "robot_type": "unknown",
+    "total_episodes": 108,
+    "total_frames": 86400,
+    "total_tasks": 1,
+    "total_videos": 324,
+    "total_chunks": 1,
+    "chunks_size": 1000,
+    "fps": 50,
+    "splits": {
+        "train": "0:108"
+    },
+    "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
+    "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
+    "features": {
+        "observation.images.left_wrist": {
+            "dtype": "video",
+            "shape": [
+                480,
+                640,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "video_info": {
+                "video.fps": 50.0,
+                "video.codec": "h264",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "has_audio": false
+            }
+        },
+        "observation.images.right_wrist": {
+            "dtype": "video",
+            "shape": [
+                480,
+                640,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "video_info": {
+                "video.fps": 50.0,
+                "video.codec": "h264",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "has_audio": false
+            }
+        },
+        "observation.images.top": {
+            "dtype": "video",
+            "shape": [
+                480,
+                640,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "video_info": {
+                "video.fps": 50.0,
+                "video.codec": "h264",
+                "video.pix_fmt": "yuv420p",
+                "video.is_depth_map": false,
+                "has_audio": false
+            }
+        },
+        "observation.state": {
+            "dtype": "float32",
+            "shape": [
+                14
+            ],
+            "names": {
+                "motors": [
+                    "motor_0",
+                    "motor_1",
+                    "motor_2",
+                    "motor_3",
+                    "motor_4",
+                    "motor_5",
+                    "motor_6",
+                    "motor_7",
+                    "motor_8",
+                    "motor_9",
+                    "motor_10",
+                    "motor_11",
+                    "motor_12",
+                    "motor_13"
+                ]
+            }
+        },
+        "observation.effort": {
+            "dtype": "float32",
+            "shape": [
+                14
+            ],
+            "names": {
+                "motors": [
+                    "motor_0",
+                    "motor_1",
+                    "motor_2",
+                    "motor_3",
+                    "motor_4",
+                    "motor_5",
+                    "motor_6",
+                    "motor_7",
+                    "motor_8",
+                    "motor_9",
+                    "motor_10",
+                    "motor_11",
+                    "motor_12",
+                    "motor_13"
+                ]
+            }
+        },
+        "action": {
+            "dtype": "float32",
+            "shape": [
+                14
+            ],
+            "names": {
+                "motors": [
+                    "motor_0",
+                    "motor_1",
+                    "motor_2",
+                    "motor_3",
+                    "motor_4",
+                    "motor_5",
+                    "motor_6",
+                    "motor_7",
+                    "motor_8",
+                    "motor_9",
+                    "motor_10",
+                    "motor_11",
+                    "motor_12",
+                    "motor_13"
+                ]
+            }
+        },
+        "episode_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "frame_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "timestamp": {
+            "dtype": "float32",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "next.done": {
+            "dtype": "bool",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "task_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        }
+    }
+}
 ```
 
-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`
-
-## Upload trained policy checkpoint
-After training the model you can upload it to Huggingface with:
-```bash
-huggingface-cli upload $hf_username/$repo_name PATH_TO_CHECKPOINT
-```
-where PATH_TO_CHECKPOINT is the folder containing the checkpoints of your training. it should look like `outputs/train/2024-10-23/23-02-55_aloha_diffusion_default/checkpoints/015000`.
-
-# Policy Evaluation
-
-
-```
-python hackathon/evaluate_pretrained_policy_hackathon.py --device cuda --pretrained-policy-name-or-path HumanoidTeam/hackathon_sim_aloha --num-videos 5  --num-rollouts 10
-```
+## Citation
 
+**BibTeX:**
 
-This dataset was created using [🤗 LeRobot](https://github.com/huggingface/lerobot).
+```bibtex
+[More Information Needed]
+```