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v0.7 (#26)

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* update from scratch configs

* update gym pretraining configs - use fewer epochs

* update robomimic pretraining configs - use fewer epochs

* allow trajectory plotting in eval agent

* add simple vit unet

* update avoid pretraining configs - use fewer epochs

* update furniture pretraining configs - use same amount of epochs as before

* add robomimic diffusion unet pretraining configs

* update robomimic finetuning configs - higher lr

* add vit unet checkpoint urls

* update pretraining and finetuning instructions as configs are updated
This commit is contained in:
Allen Z. Ren
2024-11-20 15:56:23 -05:00
committed by allenzren
parent d2929f65e1
commit 1d04211666
158 changed files with 3350 additions and 410 deletions
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cfg/robomimic/eval/transport/eval_diffusion_mlp.yaml
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defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.eval.eval_diffusion_agent.EvalDiffusionAgent
name: ${env_name}_eval_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
base_policy_path:
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/normalization.npz
seed: 42
device: cuda:0
env_name: transport
obs_dim: 59
action_dim: 14
denoising_steps: 20
cond_steps: 1
horizon_steps: 8
act_steps: 8
n_steps: 400 # each episode takes max_episode_steps / act_steps steps
render_num: 0
env:
n_envs: 50
name: ${env_name}
best_reward_threshold_for_success: 1
max_episode_steps: 800
save_video: False
wrappers:
robomimic_lowdim:
normalization_path: ${normalization_path}
low_dim_keys: ['robot0_eef_pos',
'robot0_eef_quat',
'robot0_gripper_qpos',
"robot1_eef_pos",
"robot1_eef_quat",
"robot1_gripper_qpos",
'object'] # same order of preprocessed observations
multi_step:
n_obs_steps: ${cond_steps}
n_action_steps: ${act_steps}
max_episode_steps: ${env.max_episode_steps}
reset_within_step: True
model:
_target_: model.diffusion.diffusion.DiffusionModel
predict_epsilon: True
denoised_clip_value: 1.0
randn_clip_value: 3
#
network_path: ${base_policy_path}
network:
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
time_dim: 32
mlp_dims: [1024, 1024, 1024]
residual_style: True
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
horizon_steps: ${horizon_steps}
action_dim: ${action_dim}
horizon_steps: ${horizon_steps}
obs_dim: ${obs_dim}
action_dim: ${action_dim}
denoising_steps: ${denoising_steps}
device: ${device}
cfg/robomimic/eval/transport/eval_diffusion_mlp_img.yaml
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defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.eval.eval_diffusion_img_agent.EvalImgDiffusionAgent
name: ${env_name}_eval_diffusion_mlp_img_ta${horizon_steps}_td${denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
base_policy_path:
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}-img.json
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}-img/normalization.npz
seed: 42
device: cuda:0
env_name: transport
obs_dim: 18
action_dim: 14
denoising_steps: 100
cond_steps: 1
img_cond_steps: 1
horizon_steps: 8
act_steps: 8
use_ddim: True
ddim_steps: 5
n_steps: 200 # each episode takes max_episode_steps / act_steps steps
render_num: 0
env:
n_envs: 30 # reduce gpu usage
name: ${env_name}
best_reward_threshold_for_success: 1
max_episode_steps: 800
save_video: False
use_image_obs: True
wrappers:
robomimic_image:
normalization_path: ${normalization_path}
low_dim_keys: ['robot0_eef_pos',
'robot0_eef_quat',
'robot0_gripper_qpos',
"robot1_eef_pos",
"robot1_eef_quat",
"robot1_gripper_qpos"]
image_keys: ['shouldercamera0_image',
'shouldercamera1_image']
shape_meta: ${shape_meta}
multi_step:
n_obs_steps: ${cond_steps}
n_action_steps: ${act_steps}
max_episode_steps: ${env.max_episode_steps}
reset_within_step: True
shape_meta:
obs:
rgb:
shape: [6, 96, 96]
state:
shape: [18]
action:
shape: [14]
model:
_target_: model.diffusion.diffusion.DiffusionModel
predict_epsilon: True
denoised_clip_value: 1.0
randn_clip_value: 3
#
use_ddim: ${use_ddim}
ddim_steps: ${ddim_steps}
network_path: ${base_policy_path}
network:
_target_: model.diffusion.mlp_diffusion.VisionDiffusionMLP
backbone:
_target_: model.common.vit.VitEncoder
obs_shape: ${shape_meta.obs.rgb.shape}
num_channel: ${eval:'3 * ${img_cond_steps}'} # each image patch is history concatenated
img_h: ${shape_meta.obs.rgb.shape[1]}
img_w: ${shape_meta.obs.rgb.shape[2]}
cfg:
patch_size: 8
depth: 1
embed_dim: 128
num_heads: 4
embed_style: embed2
embed_norm: 0
augment: False
num_img: 2
spatial_emb: 128
time_dim: 32
mlp_dims: [768, 768, 768]
residual_style: True
img_cond_steps: ${img_cond_steps}
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
horizon_steps: ${horizon_steps}
action_dim: ${action_dim}
horizon_steps: ${horizon_steps}
obs_dim: ${obs_dim}
action_dim: ${action_dim}
denoising_steps: ${denoising_steps}
device: ${device}
cfg/robomimic/eval/transport/eval_diffusion_unet.yaml
+71
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defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.eval.eval_diffusion_agent.EvalDiffusionAgent
name: ${env_name}_eval_diffusion_unet_ta${horizon_steps}_td${denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
base_policy_path:
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/normalization.npz
seed: 42
device: cuda:0
env_name: transport
obs_dim: 59
action_dim: 14
denoising_steps: 20
cond_steps: 1
horizon_steps: 16
act_steps: 8
n_steps: 100 # each episode takes max_episode_steps / act_steps steps
render_num: 0
env:
n_envs: 50
name: ${env_name}
best_reward_threshold_for_success: 1
max_episode_steps: 800
save_video: False
wrappers:
robomimic_lowdim:
normalization_path: ${normalization_path}
low_dim_keys: ['robot0_eef_pos',
'robot0_eef_quat',
'robot0_gripper_qpos',
"robot1_eef_pos",
"robot1_eef_quat",
"robot1_gripper_qpos",
'object'] # same order of preprocessed observations
multi_step:
n_obs_steps: ${cond_steps}
n_action_steps: ${act_steps}
max_episode_steps: ${env.max_episode_steps}
reset_within_step: True
model:
_target_: model.diffusion.diffusion.DiffusionModel
predict_epsilon: True
denoised_clip_value: 1.0
randn_clip_value: 3
#
network_path: ${base_policy_path}
network:
_target_: model.diffusion.unet.Unet1D
diffusion_step_embed_dim: 16
dim: 64
dim_mults: [1, 2]
kernel_size: 5
n_groups: 8
smaller_encoder: False
cond_predict_scale: True
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
action_dim: ${action_dim}
horizon_steps: ${horizon_steps}
obs_dim: ${obs_dim}
action_dim: ${action_dim}
denoising_steps: ${denoising_steps}
device: ${device}
cfg/robomimic/eval/transport/eval_diffusion_unet_img.yaml
+107
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defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.eval.eval_diffusion_img_agent.EvalImgDiffusionAgent
name: ${env_name}_eval_diffusion_unet_img_ta${horizon_steps}_td${denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
base_policy_path:
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}-img.json
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}-img/normalization.npz
seed: 42
device: cuda:0
env_name: transport
obs_dim: 18
action_dim: 14
denoising_steps: 100
cond_steps: 1
img_cond_steps: 1
horizon_steps: 16
act_steps: 8
use_ddim: True
ddim_steps: 5
n_steps: 400 # each episode takes max_episode_steps / act_steps steps
render_num: 0
env:
n_envs: 30 # reduce gpu usage
name: ${env_name}
best_reward_threshold_for_success: 1
max_episode_steps: 800
save_video: False
use_image_obs: True
wrappers:
robomimic_image:
normalization_path: ${normalization_path}
low_dim_keys: ['robot0_eef_pos',
'robot0_eef_quat',
'robot0_gripper_qpos',
"robot1_eef_pos",
"robot1_eef_quat",
"robot1_gripper_qpos"]
image_keys: ['shouldercamera0_image',
'shouldercamera1_image']
shape_meta: ${shape_meta}
multi_step:
n_obs_steps: ${cond_steps}
n_action_steps: ${act_steps}
max_episode_steps: ${env.max_episode_steps}
reset_within_step: True
shape_meta:
obs:
rgb:
shape: [6, 96, 96]
state:
shape: [18]
action:
shape: [14]
model:
_target_: model.diffusion.diffusion.DiffusionModel
predict_epsilon: True
denoised_clip_value: 1.0
randn_clip_value: 3
#
use_ddim: ${use_ddim}
ddim_steps: ${ddim_steps}
network_path: ${base_policy_path}
network:
_target_: model.diffusion.unet.VisionUnet1D
backbone:
_target_: model.common.vit.VitEncoder
obs_shape: ${shape_meta.obs.rgb.shape}
num_channel: ${eval:'3 * ${img_cond_steps}'} # each image patch is history concatenated
img_h: ${shape_meta.obs.rgb.shape[1]}
img_w: ${shape_meta.obs.rgb.shape[2]}
cfg:
patch_size: 8
depth: 1
embed_dim: 128
num_heads: 4
embed_style: embed2
embed_norm: 0
img_cond_steps: ${img_cond_steps}
augment: False
num_img: 2
spatial_emb: 128
diffusion_step_embed_dim: 32
dim: 64
dim_mults:
- 1
- 2
kernel_size: 5
n_groups: 8
smaller_encoder: false
cond_predict_scale: true
action_dim: ${action_dim}
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
horizon_steps: ${horizon_steps}
obs_dim: ${obs_dim}
action_dim: ${action_dim}
denoising_steps: ${denoising_steps}
device: ${device}