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import tensorflow as tf
from data.utils import clean_task_instruction
def process_step(step: dict) -> dict:
"""
Unify the action format and clean the task instruction.
DO NOT use python list, use tf.TensorArray instead.
"""
# Convert raw action to our action
origin_action = step['action']
step['action'] = {}
action = step['action']
# Multiplied by 10 Hz to get units m/s and rad/s
eef_delta_pos = origin_action[:3] * 10
# delta ZYX euler angles == roll/pitch/yaw velocities
eef_ang = origin_action[3:6] * 10
grip_open = 1 - origin_action[6:7]
# No base found
# Concatenate the action
action['arm_concat'] = tf.concat([eef_delta_pos, eef_ang, grip_open],axis=0)
# Write the action format
action['format'] = tf.constant(
"eef_vel_x,eef_vel_y,eef_vel_z,eef_angular_vel_roll,eef_angular_vel_pitch,eef_angular_vel_yaw,gripper_open")
# Convert raw state to our state
# state = step['observation']
# # Concatenate the state
# grip_open=state['state']
# state['arm_concat'] = grip_open
# Write the state format
# state['format'] = tf.constant(
# "gripper_open")
# Clean the task instruction
# Define the replacements (old, new) as a dictionary
replacements = {
'_': ' ',
'1f': ' ',
'4f': ' ',
'-': ' ',
'50': ' ',
'55': ' ',
'56': ' ',
}
instr = step['language_instruction']
instr = clean_task_instruction(instr, replacements)
step['observation']['natural_language_instruction'] = instr
return step
if __name__ == "__main__":
import tensorflow_datasets as tfds
from data.utils import dataset_to_path
DATASET_DIR = 'data/datasets/openx_embod'
DATASET_NAME = 'imperialcollege_sawyer_wrist_cam'
# Load the dataset
dataset = tfds.builder_from_directory(
builder_dir=dataset_to_path(
DATASET_NAME, DATASET_DIR))
dataset = dataset.as_dataset(split='all')
# Inspect the dataset
for episode in dataset:
for step in episode['steps']:
print(step)
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