Robotics
Transformers
Safetensors
Inference Endpoints
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resume: false
device: cuda
use_amp: true
seed: 100000
dataset_repo_id: alexandersoare/pusht_keypoints_only
video_backend: pyav
training:
offline_steps: 200000
online_steps: 0
online_steps_between_rollouts: 1
online_sampling_ratio: 0.5
online_env_seed: ???
eval_freq: 10000
log_freq: 50
save_checkpoint: true
save_freq: 20000
num_workers: 4
batch_size: 64
image_transforms:
enable: false
max_num_transforms: 3
random_order: false
brightness:
weight: 1
min_max:
- 0.8
- 1.2
contrast:
weight: 1
min_max:
- 0.8
- 1.2
saturation:
weight: 1
min_max:
- 0.5
- 1.5
hue:
weight: 1
min_max:
- -0.05
- 0.05
sharpness:
weight: 1
min_max:
- 0.8
- 1.2
grad_clip_norm: 10
lr: 0.0001
lr_scheduler: cosine
lr_warmup_steps: 500
adam_betas:
- 0.95
- 0.999
adam_eps: 1.0e-08
adam_weight_decay: 1.0e-06
delta_timestamps:
observation.environment_state:
- -0.1
- 0.0
observation.state:
- -0.1
- 0.0
action:
- -0.1
- 0.0
- 0.1
- 0.2
- 0.3
- 0.4
- 0.5
- 0.6
- 0.7
- 0.8
- 0.9
- 1.0
- 1.1
- 1.2
- 1.3
- 1.4
drop_n_last_frames: 7
eval:
n_episodes: 50
batch_size: 50
use_async_envs: false
wandb:
enable: true
disable_artifact: true
project: lerobot
notes: ''
fps: 10
env:
name: pusht
task: PushT-v0
image_size: 96
state_dim: 2
action_dim: 2
fps: ${fps}
episode_length: 300
gym:
obs_type: environment_state_agent_pos
render_mode: rgb_array
visualization_width: 384
visualization_height: 384
policy:
name: diffusion
n_obs_steps: 2
horizon: 16
n_action_steps: 8
input_shapes:
observation.environment_state:
- 16
observation.state:
- ${env.state_dim}
output_shapes:
action:
- ${env.action_dim}
input_normalization_modes:
observation.environment_state: min_max
observation.state: min_max
output_normalization_modes:
action: min_max
vision_backbone: resnet18
crop_shape:
- 84
- 84
crop_is_random: true
pretrained_backbone_weights: null
use_group_norm: true
spatial_softmax_num_keypoints: 32
down_dims:
- 512
- 1024
- 2048
kernel_size: 5
n_groups: 8
diffusion_step_embed_dim: 128
use_film_scale_modulation: true
noise_scheduler_type: DDIM
num_train_timesteps: 100
beta_schedule: squaredcos_cap_v2
beta_start: 0.0001
beta_end: 0.02
prediction_type: epsilon
clip_sample: true
clip_sample_range: 1.0
num_inference_steps: 10
do_mask_loss_for_padding: false