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_target_: pfp.policy.fm_policy.FMPolicy
x_dim: ${x_dim}
y_dim: ${y_dim}
n_obs_steps: ${n_obs_steps}
n_pred_steps: ${n_pred_steps}
num_k_infer: 10
time_conditioning: True
norm_pcd_center: [0.4, 0.0, 1.4]
augment_data: False
noise_type: gaussian # gaussian | trajectory | igso3
noise_scale: 1.0
loss_type: l2 # l2 | l1
flow_schedule: exp # linear | cosine | exp
exp_scale: 4.0
snr_sampler: uniform # uniform | logit_normal
obs_encoder: ${backbone}
diffusion_net:
_target_: diffusion_policy.model.diffusion.conditional_unet1d.ConditionalUnet1D
input_dim: ${y_dim}
global_cond_dim: "${eval: '${x_dim} * ${n_obs_steps}'}"
diffusion_step_embed_dim: "${eval: '256 if ${model.time_conditioning} else 0'}"
down_dims: [256, 512, 1024]
kernel_size: 5
n_groups: 8
cond_predict_scale: True
use_dropout: False
loss_weights:
xyz: 10.0
rot6d: 10.0
grip: 1.0