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import tensorflow as tf
from data.utils import clean_task_instruction, euler_to_quaternion, \
quaternion_to_rotation_matrix, rotation_matrix_to_ortho6d
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']
action['terminate'] = step['is_terminal']
# 6x end effector delta pose, 1x gripper position
eef_delta_pos = origin_action[:3]
eef_ang=origin_action[3:6]
eef_ang = euler_to_quaternion(eef_ang)
# No base found
# Concatenate the action
action['arm_concat'] = tf.concat([eef_delta_pos,eef_ang],axis=0)
# Write the action format
action['format'] = tf.constant(
"eef_delta_pos_x,eef_delta_pos_y,eef_delta_pos_z,eef_delta_angle_x,eef_delta_angle_y,eef_delta_angle_z,eef_delta_angle_w")
# Convert raw state to our state
state = step['observation']
# Concatenate the state
# [6x robot joint angles, 1x gripper open status, 6x robot joint velocities].
arm_joint_ang=state['state'][:6]
grip_open=1-state['state'][6:7]
# arm_joint_vel=state['state'][7:13] # all zeros
eef_pos = state['end_effector_state'][:3]
eef_ang = quaternion_to_rotation_matrix(state['end_effector_state'][3:])
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
state['arm_concat'] = tf.concat([arm_joint_ang,grip_open,eef_pos,eef_ang],axis=0)
# Write the state format
state['format'] = tf.constant(
"arm_joint_0_pos,arm_joint_1_pos,arm_joint_2_pos,arm_joint_3_pos,arm_joint_4_pos,arm_joint_5_pos,gripper_open,eef_pos_x,eef_pos_y,eef_pos_z,eef_angle_0,eef_angle_1,eef_angle_2,eef_angle_3,eef_angle_4,eef_angle_5")
# 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 = 'berkeley_fanuc_manipulation'
# 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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