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lerobot
/
diffusion_pusht

Robotics
Transformers
Safetensors
Inference Endpoints
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Files changed (9) hide show
  1. README.md +39 -0
  2. config.json +53 -0
  3. config.yaml +137 -0
  4. eval_avg_max_reward.csv +21 -0
  5. eval_info.json +3523 -0
  6. eval_pc_success.csv +21 -0
  7. model.safetensors +3 -0
  8. train_loss.csv +801 -0
  9. training_curves.png +0 -0
README.md ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Model Card for Diffusion Policy / PushT
2
+
3
+ Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
4
+ Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht).
5
+
6
+ <!-- TODO demo -->
7
+
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+ ## How to Get Started with the Model
9
+
10
+ See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)) for instructions on how to load and evaluate this model.
11
+
12
+ ## Training Details
13
+
14
+ TODO commit hash.
15
+
16
+ Trained with [LeRobot@d747195](https://github.com/huggingface/lerobot/tree/d747195c5733c4f68d4bfbe62632d6fc1b605712).
17
+
18
+ The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3) dataset.
19
+
20
+ Here are the [loss](./train_loss.csv), [evaluation score](./eval_avg_max_reward.csv), [evaluation success rate](./eval_pc_success.csv) (with 50 rollouts) during training.
21
+
22
+ ![](training_curves.png)
23
+
24
+ This took about 7 hours to train on an Nvida RTX 3090.
25
+
26
+ ## Evaluation
27
+
28
+ The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) and compared to a similar model trained with the original [Diffusion Policy code](https://github.com/real-stanford/diffusion_policy). There are two evaluation metrics on a per-episode basis:
29
+
30
+ - Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
31
+ - Success: whether or not the maximum overlap is at least 95%.
32
+
33
+ Here are the metrics for 500 episodes worth of evaluation. For the succes rate we add and extra row with confidence bounds. This assumes a uniform prior over success probability and computes the beta posterior, then calculates the mean and lower/upper confidence bounds (with a 68.2% confidence interval centered on the mean).
34
+
35
+ <blank>|Ours|Theirs
36
+ -|-|-
37
+ Average max. overlap ratio | 0.959 | 0.957
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+ Success rate for 500 episodes (%) | 63.8 | 64.2
39
+ Beta distribution lower/mean/upper (%) | 61.6 / 63.7 / 65.9 | 62.0 / 64.1 / 66.3
config.json ADDED
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+ {
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+ "horizon": 16,
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+ "input_shapes": {
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+ "observation.image": [
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+ 96
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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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+ "output_shapes": {
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+ "action": [
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+ 2
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+ ]
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+ },
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+ "input_normalization_modes": {
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+ "observation.image": "mean_std",
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+ "observation.state": "min_max"
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+ },
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+ "output_normalization_modes": {
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+ "action": "min_max"
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+ },
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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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+ ],
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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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+ ],
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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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+ "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": 100
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+ }
config.yaml ADDED
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+ device: cuda
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+ seed: 100000
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+ dataset_repo_id: lerobot/pusht
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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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+ save_freq: 20000
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+ log_freq: 250
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+ save_model: true
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+ batch_size: 64
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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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+ observation.image:
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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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+ - 0.0
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+ action:
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+ - -0.1
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+ - 0.0
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+ - 0.1
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+ - 0.2
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+ - 0.3
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+ - 0.4
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+ - 0.5
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+ - 0.7
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+ - 1.0
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+ - 1.3
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+ - 1.4
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+ n_end_keyframes_dropped: ${policy.horizon} - ${policy.n_action_steps} - ${policy.n_obs_steps}
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+ + 1
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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
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+ wandb:
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+ enable: true
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+ disable_artifact: true
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+ project: lerobot
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+ notes: ''
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+ fps: 10
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+ env:
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+ name: pusht
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+ task: PushT-v0
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+ from_pixels: true
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+ pixels_only: false
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+ image_size: 96
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+ episode_length: 300
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+ fps: ${fps}
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+ state_dim: 2
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+ action_dim: 2
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+ override_dataset_stats:
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+ observation.image:
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+ mean:
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+ - - - 0.5
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+ - - - 0.5
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+ - - - 0.5
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+ std:
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+ - - - 0.5
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+ min:
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+ - 511.0
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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.image:
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+ - 3
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+ - 96
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+ - 96
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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.image: mean_std
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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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+ 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: 100
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+ 190000,0.963914309028651
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eval_info.json ADDED
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