Add policy/act_aloha_real.yaml + env/act_real.yaml (#429)
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
This commit is contained in:
Remi
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lerobot/configs/env/aloha_real.yaml
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lerobot/configs/env/aloha_real.yaml
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# @package _global_
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fps: 30
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env:
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name: real_world
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task: null
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state_dim: 18
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action_dim: 18
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fps: ${fps}
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@@ -1,16 +1,22 @@
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# @package _global_
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# Use `act_real.yaml` to train on real-world Aloha/Aloha2 datasets.
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# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, images,
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# cam_low) instead of 1 camera (i.e. top). Also, `training.eval_freq` is set to -1. This config is used
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# to evaluate checkpoints at a certain frequency of training steps. When it is set to -1, it deactivates evaluation.
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# This is because real-world evaluation is done through [dora-lerobot](https://github.com/dora-rs/dora-lerobot).
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# Look at its README for more information on how to evaluate a checkpoint in the real-world.
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# Use `act_aloha_real.yaml` to train on real-world datasets collected on Aloha or Aloha-2 robots.
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# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, cam_high, cam_low) instead of 1 camera (i.e. top).
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# Also, `training.eval_freq` is set to -1. This config is used to evaluate checkpoints at a certain frequency of training steps.
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# When it is set to -1, it deactivates evaluation. This is because real-world evaluation is done through our `control_robot.py` script.
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# Look at the documentation in header of `control_robot.py` for more information on how to collect data , train and evaluate a policy.
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#
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# Example of usage for training:
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# Example of usage for training and inference with `control_robot.py`:
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# ```bash
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# python lerobot/scripts/train.py \
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# policy=act_real \
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# policy=act_aloha_real \
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# env=aloha_real
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# ```
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#
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# Example of usage for training and inference with [Dora-rs](https://github.com/dora-rs/dora-lerobot):
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# ```bash
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# python lerobot/scripts/train.py \
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# policy=act_aloha_real \
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# env=dora_aloha_real
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# ```
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@@ -36,10 +42,11 @@ override_dataset_stats:
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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training:
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offline_steps: 100000
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offline_steps: 80000
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online_steps: 0
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eval_freq: -1
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save_freq: 20000
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save_freq: 10000
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log_freq: 100
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save_checkpoint: true
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batch_size: 8
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@@ -62,7 +69,7 @@ policy:
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# Input / output structure.
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n_obs_steps: 1
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chunk_size: 100 # chunk_size
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chunk_size: 100
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n_action_steps: 100
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input_shapes:
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@@ -107,7 +114,7 @@ policy:
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n_vae_encoder_layers: 4
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# Inference.
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temporal_ensemble_coeff: null
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temporal_ensemble_momentum: null
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# Training and loss computation.
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dropout: 0.1
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@@ -1,110 +0,0 @@
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# @package _global_
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# Use `act_real_no_state.yaml` to train on real-world Aloha/Aloha2 datasets when cameras are moving (e.g. wrist cameras)
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# Compared to `act_real.yaml`, it is camera only and does not use the state as input which is vector of robot joint positions.
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# We validated experimentaly that not using state reaches better success rate. Our hypothesis is that `act_real.yaml` might
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# overfits to the state, because the images are more complex to learn from since they are moving.
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#
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# Example of usage for training:
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# ```bash
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# python lerobot/scripts/train.py \
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# policy=act_real_no_state \
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# env=dora_aloha_real
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# ```
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seed: 1000
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dataset_repo_id: lerobot/aloha_static_vinh_cup
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override_dataset_stats:
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observation.images.cam_right_wrist:
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# stats from imagenet, since we use a pretrained vision model
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mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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observation.images.cam_left_wrist:
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# stats from imagenet, since we use a pretrained vision model
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mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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observation.images.cam_high:
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# stats from imagenet, since we use a pretrained vision model
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mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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observation.images.cam_low:
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# stats from imagenet, since we use a pretrained vision model
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mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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training:
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offline_steps: 100000
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online_steps: 0
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eval_freq: -1
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save_freq: 20000
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save_checkpoint: true
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batch_size: 8
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lr: 1e-5
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lr_backbone: 1e-5
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weight_decay: 1e-4
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grad_clip_norm: 10
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online_steps_between_rollouts: 1
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delta_timestamps:
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action: "[i / ${fps} for i in range(${policy.chunk_size})]"
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eval:
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n_episodes: 50
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batch_size: 50
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# See `configuration_act.py` for more details.
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policy:
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name: act
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# Input / output structure.
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n_obs_steps: 1
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chunk_size: 100 # chunk_size
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n_action_steps: 100
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input_shapes:
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# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
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observation.images.cam_right_wrist: [3, 480, 640]
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observation.images.cam_left_wrist: [3, 480, 640]
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observation.images.cam_high: [3, 480, 640]
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observation.images.cam_low: [3, 480, 640]
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output_shapes:
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action: ["${env.action_dim}"]
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# Normalization / Unnormalization
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input_normalization_modes:
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observation.images.cam_right_wrist: mean_std
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observation.images.cam_left_wrist: mean_std
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observation.images.cam_high: mean_std
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observation.images.cam_low: mean_std
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output_normalization_modes:
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action: mean_std
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# Architecture.
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# Vision backbone.
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vision_backbone: resnet18
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pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
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replace_final_stride_with_dilation: false
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# Transformer layers.
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pre_norm: false
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dim_model: 512
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n_heads: 8
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dim_feedforward: 3200
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feedforward_activation: relu
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n_encoder_layers: 4
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# Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
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# that means only the first layer is used. Here we match the original implementation by setting this to 1.
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# See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
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n_decoder_layers: 1
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# VAE.
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use_vae: true
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latent_dim: 32
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n_vae_encoder_layers: 4
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# Inference.
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temporal_ensemble_coeff: null
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# Training and loss computation.
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dropout: 0.1
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kl_weight: 10.0
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