robotics_transformer/trained_checkpoints/rt1simreal/policy_specs.pbtxt

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2022-12-14 06:19:44 +08:00
dict_value {
fields {
key: "collect_data_spec"
value {
named_tuple_value {
name: "Trajectory"
values {
key: "step_type"
value {
tensor_spec_value {
name: "step_type"
shape {
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dtype: DT_INT32
}
}
}
values {
key: "observation"
value {
dict_value {
fields {
key: "base_pose_tool_reached"
value {
tensor_spec_value {
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fields {
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tensor_spec_value {
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shape {
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}
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fields {
key: "gripper_closedness_commanded"
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tensor_spec_value {
name: "gripper_closedness_commanded"
shape {
dim {
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dtype: DT_FLOAT
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}
fields {
key: "height_to_bottom"
value {
tensor_spec_value {
name: "height_to_bottom"
shape {
dim {
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dtype: DT_FLOAT
}
}
}
fields {
key: "image"
value {
tensor_spec_value {
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shape {
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dim {
size: 320
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dim {
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dtype: DT_UINT8
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}
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fields {
key: "natural_language_embedding"
value {
tensor_spec_value {
name: "natural_language_embedding"
shape {
dim {
size: 512
}
}
dtype: DT_FLOAT
}
}
}
fields {
key: "natural_language_instruction"
value {
tensor_spec_value {
name: "natural_language_instruction"
shape {
}
dtype: DT_STRING
}
}
}
fields {
key: "orientation_box"
value {
tensor_spec_value {
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shape {
dim {
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}
dim {
size: 3
}
}
dtype: DT_FLOAT
}
}
}
fields {
key: "orientation_start"
value {
tensor_spec_value {
name: "orientation_in_camera_space"
shape {
dim {
size: 4
}
}
dtype: DT_FLOAT
}
}
}
fields {
key: "robot_orientation_positions_box"
value {
tensor_spec_value {
name: "robot_orientation_positions_box"
shape {
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dim {
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}
fields {
key: "rotation_delta_to_go"
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tensor_spec_value {
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dtype: DT_FLOAT
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fields {
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tensor_spec_value {
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}
dtype: DT_FLOAT
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fields {
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value {
tensor_spec_value {
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dim {
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}
dtype: DT_FLOAT
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}
fields {
key: "workspace_bounds"
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tensor_spec_value {
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dim {
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dtype: DT_FLOAT
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}
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values {
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fields {
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value {
bounded_tensor_spec_value {
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shape {
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float_val: 1.0
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fields {
key: "base_displacement_vertical_rotation"
value {
bounded_tensor_spec_value {
name: "base_displacement_vertical_rotation"
shape {
dim {
size: 1
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dtype: DT_FLOAT
minimum {
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float_val: -3.1415927
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maximum {
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tensor_shape {
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float_val: 3.1415927
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}
fields {
key: "gripper_closedness_action"
value {
bounded_tensor_spec_value {
name: "gripper_closedness_action"
shape {
dim {
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}
dtype: DT_FLOAT
minimum {
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tensor_shape {
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float_val: -1.0
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maximum {
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tensor_shape {
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float_val: 1.0
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}
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fields {
key: "rotation_delta"
value {
bounded_tensor_spec_value {
name: "rotation_delta"
shape {
dim {
size: 3
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dtype: DT_FLOAT
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float_val: -1.5707964
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maximum {
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tensor_shape {
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float_val: 1.5707964
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}
fields {
key: "terminate_episode"
value {
bounded_tensor_spec_value {
name: "terminate_episode"
shape {
dim {
size: 3
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}
dtype: DT_INT32
minimum {
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tensor_shape {
}
int_val: 0
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maximum {
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tensor_shape {
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int_val: 1
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}
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}
fields {
key: "world_vector"
value {
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dtype: DT_FLOAT
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}
values {
key: "policy_info"
value {
dict_value {
fields {
key: "discounted_return"
value {
bounded_tensor_spec_value {
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shape {
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float_val: 0.0
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maximum {
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fields {
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dtype: DT_FLOAT
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tensor_shape {
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maximum {
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values {
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tensor_spec_value {
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shape {
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dtype: DT_INT32
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}
values {
key: "reward"
value {
tensor_spec_value {
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shape {
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dtype: DT_FLOAT
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values {
key: "discount"
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bounded_tensor_spec_value {
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shape {
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dtype: DT_FLOAT
minimum {
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tensor_shape {
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float_val: 0.0
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maximum {
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float_val: 1.0
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}
fields {
key: "policy_state_spec"
value {
dict_value {
fields {
key: "action_tokens"
value {
tensor_spec_value {
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shape {
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dim {
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dim {
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dim {
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dtype: DT_INT32
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fields {
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value {
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shape {
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dim {
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dim {
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dim {
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fields {
key: "step_num"
value {
tensor_spec_value {
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shape {
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dim {
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dim {
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dtype: DT_INT32
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fields {
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dim {
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}