9d002032d1
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm) poetry lock fix bug in compute_stats for action normalization fix more bugs in normalization fix training fix import PushtEnv inheriates AbstractEnv, Improve factory Normalization Add _make_env to EnvAbstract Add call_rendering_hooks to pusht env SimxarmEnv inherites from AbstractEnv (NOT TESTED) Add aloha tests artifacts + update pusht stats fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255] Small fix on simxarm Add next to obs Add top camera to Aloha env (TODO: make it compatible with set of cameras) Add top camera to Aloha env (TODO: make it compatible with set of cameras)
59 lines
939 B
YAML
59 lines
939 B
YAML
# @package _global_
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offline_steps: 1344000
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online_steps: 0
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eval_episodes: 1
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eval_freq: 10000
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save_freq: 100000
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log_freq: 250
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horizon: 100
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n_obs_steps: 1
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n_latency_steps: 0
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# when temporal_agg=False, n_action_steps=horizon
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n_action_steps: ${horizon}
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policy:
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name: act
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pretrained_model_path:
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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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backbone: resnet18
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num_queries: ${horizon} # chunk_size
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horizon: ${horizon} # chunk_size
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kl_weight: 10
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hidden_dim: 512
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dim_feedforward: 3200
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enc_layers: 4
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dec_layers: 7
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nheads: 8
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#camera_names: [top, front_close, left_pillar, right_pillar]
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camera_names: [top]
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position_embedding: sine
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masks: false
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dilation: false
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dropout: 0.1
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pre_norm: false
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vae: true
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batch_size: 8
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per_alpha: 0.6
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per_beta: 0.4
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balanced_sampling: false
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utd: 1
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n_obs_steps: ${n_obs_steps}
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temporal_agg: false
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state_dim: ???
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action_dim: ???
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