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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 terminate_act_to_bool(terminate_act: tf.Tensor) -> tf.Tensor:
"""
Convert terminate action to a boolean, where True means terminate.
"""
return tf.where(tf.equal(terminate_act, tf.constant(0.0, dtype=tf.float32)),tf.constant(False),tf.constant(True))
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
action = step['action']
action['terminate'] = terminate_act_to_bool(action['terminate_episode'])
eef_delta_pos = action['world_vector']
eef_ang = action['rotation_delta']
eef_ang = euler_to_quaternion(eef_ang)
# Ignore action['gripper_open']: 1 if close gripper, -1 if open gripper, 0 if no change.
# No base found
# Concatenate the action
arm_action = tf.concat([eef_delta_pos, eef_ang], axis=0)
action['arm_concat'] = arm_action
# 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']
# state['robot_state']:[joint0, joint1, joint2, joint3, joint4, joint5, x,y,z, qx,qy,qz,qw, gripper_is_closed, action_blocked]
robot_state = state['robot_state']
joint_pos=robot_state[:6]
eef_pos = robot_state[6:9]
eef_quat = robot_state[9:13]
eef_ang = quaternion_to_rotation_matrix(eef_quat)
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
# gripper_is_closed is binary: 0 = fully open; 1 = fully closed
grip_closed = robot_state[13:14]
grip_open= 1-grip_closed
# action_blocked is binary: 0 = not blocked; 1 = blocked
# action_blocked = robot_state[14:15]
# Concatenate the state
state['arm_concat'] = tf.concat([joint_pos, 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['observation']['natural_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_autolab_ur5'
# 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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