Hugging Face's logo

lerobot
/
diffusion_pusht_keypoints

Robotics
Transformers
Safetensors
Inference Endpoints
Model card Files Community
alexandersoare commited on
Commit
e6d30a8
•
1 Parent(s): ec6d3e1

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Files changed (6) hide show
  1. README.md +62 -0
  2. config.json +53 -0
  3. config.yaml +148 -0
  4. eval_info.json +3523 -0
  5. model.safetensors +3 -0
  6. replay.mp4 +0 -0
README.md ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ datasets:
4
+ - lerobot/pusht_keypoints
5
+ pipeline_tag: robotics
6
+ ---
7
+ # Model Card for Diffusion Policy / PushT (keypoints)
8
+
9
+ Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
10
+ Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) with keypoint-only observations.
11
+
12
+ ## How to Get Started with the Model
13
+
14
+ Use `python lerobot/scripts/eval.py -p lerobot/diffusion_pusht` to evaluate for 50 episodes with the outputs sent to `outputs/eval`.
15
+
16
+ For further information, please see the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)).
17
+
18
+ ## Training Details
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+
20
+ Trained with TODO commit hash
21
+
22
+ The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht_keypoints](https://huggingface.co/datasets/lerobot/pusht_keypoints/tree/v1.5) dataset, using this command:
23
+
24
+ ```bash
25
+ python lerobot/scripts/train.py \
26
+ hydra.job.name=diffusion_pusht_keypoints \
27
+ hydra.run.dir=outputs/train/2024-07-03/13-52-44_diffusion_pusht_keypoints \
28
+ env=pusht_keypoints \
29
+ policy=diffusion_pusht_keypoints \
30
+ training.save_checkpoint=true \
31
+ training.offline_steps=200000 \
32
+ training.save_freq=20000 \
33
+ training.eval_freq=10000 \
34
+ training.log_freq=50 \
35
+ training.num_workers=4 \
36
+ eval.n_episodes=50 \
37
+ eval.batch_size=50 \
38
+ wandb.enable=true \
39
+ wandb.disable_artifact=true \
40
+ device=cuda \
41
+ use_amp=true
42
+ ```
43
+
44
+ The training curves may be found at https://wandb.ai/alexander-soare/lerobot/runs/5z9d8q9q/overview.
45
+
46
+ This took about 5 hours to train on an Nvida RTX H100.
47
+
48
+ ## Evaluation
49
+
50
+ The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). There are two evaluation metrics on a per-episode basis:
51
+
52
+ - Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
53
+ - Success: whether or not the maximum overlap is at least 95%.
54
+
55
+ Here are the metrics for 500 episodes worth of evaluation.
56
+
57
+ Metric|Average over 500 episodes
58
+ -|-|-
59
+ Average max. overlap ratio | 0.97
60
+ Success rate (%) | 71.0
61
+
62
+ The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
config.json ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "beta_end": 0.02,
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+ "beta_schedule": "squaredcos_cap_v2",
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+ "beta_start": 0.0001,
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+ "clip_sample": true,
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+ "clip_sample_range": 1.0,
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+ "crop_is_random": true,
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+ "crop_shape": [
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+ 84,
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+ 84
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+ ],
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+ "diffusion_step_embed_dim": 128,
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+ "do_mask_loss_for_padding": false,
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+ "down_dims": [
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+ 512,
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+ 1024,
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+ 2048
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+ ],
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+ "horizon": 16,
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+ "input_normalization_modes": {
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+ "observation.environment_state": "min_max",
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+ "observation.state": "min_max"
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+ },
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+ "input_shapes": {
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+ "observation.environment_state": [
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+ 16
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+ ],
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+ "observation.state": [
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+ 2
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+ ]
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+ },
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+ "kernel_size": 5,
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+ "n_action_steps": 8,
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+ "n_groups": 8,
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+ "n_obs_steps": 2,
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+ "noise_scheduler_type": "DDIM",
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+ "num_inference_steps": 10,
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+ "num_train_timesteps": 100,
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+ "output_normalization_modes": {
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+ "action": "min_max"
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+ },
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+ "output_shapes": {
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+ "action": [
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+ 2
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+ ]
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+ },
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+ "prediction_type": "epsilon",
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+ "pretrained_backbone_weights": null,
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+ "spatial_softmax_num_keypoints": 32,
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+ "use_film_scale_modulation": true,
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+ "use_group_norm": true,
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+ "vision_backbone": "resnet18"
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+ }
config.yaml ADDED
@@ -0,0 +1,148 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ resume: false
2
+ device: cuda
3
+ use_amp: true
4
+ seed: 100000
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+ dataset_repo_id: alexandersoare/pusht_keypoints_only
6
+ video_backend: pyav
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+ training:
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+ offline_steps: 200000
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+ online_steps: 0
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+ online_steps_between_rollouts: 1
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+ online_sampling_ratio: 0.5
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+ online_env_seed: ???
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+ eval_freq: 10000
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+ log_freq: 50
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+ save_checkpoint: true
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+ save_freq: 20000
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+ num_workers: 4
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+ batch_size: 64
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+ image_transforms:
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+ enable: false
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+ max_num_transforms: 3
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+ random_order: false
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+ brightness:
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+ weight: 1
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+ min_max:
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+ - 0.8
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+ - 1.2
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+ contrast:
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+ weight: 1
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+ min_max:
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+ - 0.8
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+ - 1.2
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+ saturation:
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+ weight: 1
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+ min_max:
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+ - 0.5
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+ - 1.5
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+ hue:
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+ weight: 1
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+ min_max:
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+ - -0.05
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+ - 0.05
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+ sharpness:
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+ weight: 1
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+ min_max:
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+ - 0.8
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+ - 1.2
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+ grad_clip_norm: 10
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+ lr: 0.0001
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+ lr_scheduler: cosine
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+ lr_warmup_steps: 500
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+ adam_betas:
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+ - 0.95
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+ - 0.999
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+ adam_eps: 1.0e-08
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+ adam_weight_decay: 1.0e-06
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+ delta_timestamps:
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+ - -0.1
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+ - 0.0
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+ observation.state:
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+ - -0.1
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+ action:
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+ - 0.0
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+ - 0.1
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+ - 0.4
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+ - 0.7
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+ - 0.9
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+ - 1.0
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+ - 1.1
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+ - 1.2
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+ - 1.3
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+ - 1.4
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+ drop_n_last_frames: 7
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+ eval:
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+ n_episodes: 50
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+ batch_size: 50
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+ use_async_envs: false
86
+ wandb:
87
+ enable: true
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+ disable_artifact: true
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+ project: lerobot
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+ notes: ''
91
+ fps: 10
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+ env:
93
+ name: pusht
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+ task: PushT-v0
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+ image_size: 96
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+ state_dim: 2
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+ action_dim: 2
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+ fps: ${fps}
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+ episode_length: 300
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+ gym:
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+ obs_type: environment_state_agent_pos
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+ render_mode: rgb_array
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+ visualization_width: 384
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+ visualization_height: 384
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+ policy:
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+ name: diffusion
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+ n_obs_steps: 2
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+ horizon: 16
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+ n_action_steps: 8
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+ input_shapes:
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+ observation.environment_state:
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+ - 16
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+ observation.state:
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+ - ${env.state_dim}
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+ output_shapes:
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+ action:
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+ - ${env.action_dim}
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+ input_normalization_modes:
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+ observation.environment_state: min_max
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+ observation.state: min_max
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+ output_normalization_modes:
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+ action: min_max
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+ vision_backbone: resnet18
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+ crop_shape:
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+ - 84
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+ - 84
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+ crop_is_random: true
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+ pretrained_backbone_weights: null
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+ use_group_norm: true
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+ spatial_softmax_num_keypoints: 32
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+ down_dims:
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+ - 512
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+ - 1024
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+ - 2048
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+ kernel_size: 5
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+ n_groups: 8
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+ diffusion_step_embed_dim: 128
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+ use_film_scale_modulation: true
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+ noise_scheduler_type: DDIM
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+ num_train_timesteps: 100
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+ beta_schedule: squaredcos_cap_v2
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+ beta_start: 0.0001
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+ beta_end: 0.02
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+ prediction_type: epsilon
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+ clip_sample: true
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+ clip_sample_range: 1.0
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+ num_inference_steps: 10
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+ do_mask_loss_for_padding: false
eval_info.json ADDED
@@ -0,0 +1,3523 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 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+ {
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+ "success": true,
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