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import tensorflow as tf
from data.utils import clean_task_instruction, 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.equal(terminate_act, tf.constant(1.0, dtype=tf.float32))
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']
# No gripper_open found
# No base found
# Concatenate the action
arm_action=tf.concat([eef_delta_pos,eef_ang],axis=0)
action['arm_concat']=arm_action
#base_action = tf.concat([base_pos, base_ang], axis=0)
#action['base_concat'] = base_action
# 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")
# Convert raw state to our state
state = step['observation']
eef_pos = state['robot_state'][:3]
eef_ang = quaternion_to_rotation_matrix(state['robot_state'][3:])
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
# Concatenate the state
state['arm_concat']=tf.concat([eef_pos,eef_ang],axis=0)
# Write the state format
state['format'] = tf.constant(
"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")
# Define the task instruction
step['observation']['natural_language_instruction'] = tf.constant(
"Route cable through the tight-fitting clip mounted on the table.")
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_cable_routing'
# 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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