| _target_: pfp.policy.ddim_policy.DDIMPolicy | |
| x_dim: ${x_dim} | |
| y_dim: ${y_dim} | |
| n_obs_steps: ${n_obs_steps} | |
| n_pred_steps: ${n_pred_steps} | |
| num_k_train: 100 | |
| num_k_infer: 10 | |
| norm_pcd_center: [0.4, 0.0, 1.4] | |
| augment_data: False | |
| 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: 256 | |
| down_dims: [256, 512, 1024] | |
| kernel_size: 5 | |
| n_groups: 8 | |
| cond_predict_scale: True | |
| noise_scheduler_train: | |
| _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler | |
| num_train_timesteps: ${model.num_k_train} | |
| beta_start: 0.0001 | |
| beta_end: 0.02 | |
| beta_schedule: squaredcos_cap_v2 | |
| clip_sample: True | |
| set_alpha_to_one: True | |
| steps_offset: 0 | |
| prediction_type: epsilon | |
| # rescale_betas_zero_snr: True | |
| # prediction_type: v_prediction | |
| # timestep_spacing: trailing | |
| loss_weights: | |
| xyz: 10.0 | |
| rot6d: 10.0 | |
| grip: 1.0 | |