forked from tangger/lerobot
75 lines
3.1 KiB
YAML
75 lines
3.1 KiB
YAML
robot_env: {
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# TODO change the path to the correct one
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rm_left_arm: '/home/rm/aloha/shadow_rm_aloha/config/rm_left_arm.yaml',
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rm_right_arm: '/home/rm/aloha/shadow_rm_aloha/config/rm_right_arm.yaml',
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arm_axis: 6,
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head_camera: '215222076892',
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bottom_camera: '215222076981',
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left_camera: '152122078151',
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right_camera: '152122073489',
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# init_left_arm_angle: [0.226, 21.180, 91.304, -0.515, 67.486, 2.374, 0.9],
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# init_right_arm_angle: [-1.056, 33.057, 84.376, -0.204, 66.357, -3.236, 0.9]
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init_left_arm_angle: [6.45, 66.093, 2.9, 20.919, -1.491, 100.756, 18.808, 0.617],
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init_right_arm_angle: [166.953, -33.575, -163.917, 73.3, -9.581, 69.51, 0.876]
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}
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dataset_dir: '/home/rm/aloha/shadow_rm_aloha/data/dataset/20250103'
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checkpoint_dir: '/home/rm/aloha/shadow_rm_act/data'
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# checkpoint_name: 'policy_best.ckpt'
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checkpoint_name: 'policy_9500.ckpt'
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state_dim: 14
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save_episode: True
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num_rollouts: 50 #训练期间要收集的 rollout（轨迹）数量
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real_robot: True
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policy_class: 'ACT'
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onscreen_render: False
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camera_names: ['cam_high', 'cam_low', 'cam_left', 'cam_right']
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episode_len: 300 #episode 的最大长度（时间步数）。
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task_name: 'aloha_01_11.28'
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temporal_agg: False #是否使用时间聚合
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batch_size: 8 #训练期间每批的样本数。
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seed: 1000 #随机种子。
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chunk_size: 30 #用于处理序列的块大小
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eval_every: 1 #每隔 eval_every 步评估一次模型。
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num_steps: 10000 #训练的总步数。
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validate_every: 1 #每隔 validate_every 步验证一次模型。
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save_every: 500 #每隔 save_every 步保存一次检查点。
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load_pretrain: False #是否加载预训练模型。
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resume_ckpt_path:
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name_filter: # TODO
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skip_mirrored_data: False #是否跳过镜像数据（例如用于基于对称性的数据增强）。
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stats_dir:
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sample_weights:
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train_ratio: 0.8 #用于训练的数据比例（其余数据用于验证）
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policy_config: {
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hidden_dim: 512, # Size of the embeddings (dimension of the transformer)
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state_dim: 14, # Dimension of the state
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position_embedding: 'sine', # ('sine', 'learned').Type of positional embedding to use on top of the image features
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lr_backbone: 1.0e-5,
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masks: False, # If true, the model masks the non-visible pixels
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backbone: 'resnet18',
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dilation: False, # If true, we replace stride with dilation in the last convolutional block (DC5)
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dropout: 0.1, # Dropout applied in the transformer
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nheads: 8,
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dim_feedforward: 3200, # Intermediate size of the feedforward layers in the transformer blocks
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enc_layers: 4, # Number of encoding layers in the transformer
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dec_layers: 7, # Number of decoding layers in the transformer
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pre_norm: False, # If true, apply LayerNorm to the input instead of the output of the MultiheadAttention and FeedForward
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num_queries: 30,
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camera_names: ['cam_high', 'cam_low', 'cam_left', 'cam_right'],
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vq: False,
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vq_class: none,
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vq_dim: 64,
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action_dim: 14,
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no_encoder: False,
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lr: 1.0e-5,
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weight_decay: 1.0e-4,
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kl_weight: 10,
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# lr_drop: 200,
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# clip_max_norm: 0.1,
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}
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