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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.reduce_all(tf.equal(terminate_act, tf.constant([1, 0, 0], dtype=tf.int32)))
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']
# (NOTE) due to the formality problem, grip_open is not used
# grip_open = 1 - (action['gripper_closedness_action'] ) / 2
# base_delta_pos = action['base_displacement_vector']
# base_delta_ang = action['base_displacement_vertical_rotation']
# Concatenate the action
arm_action = eef_delta_pos
action['arm_concat'] = arm_action
# base_action = tf.constant([0, 0, 0, 0], dtype=tf.float32)
# action['base_concat'] = None
# Write the action format
action['format'] = tf.constant(
"eef_delta_pos_x,eef_delta_pos_y,eef_delta_pos_z")
# Convert raw state to our state
state = step['observation']
joint_pos = state['joint_pos']
eef_pos = state['end_effector_cartesian_pos'][:3]
eef_quat = state['end_effector_cartesian_pos'][3:]
eef_ang = quaternion_to_rotation_matrix(eef_quat)
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
eef_vel = state['end_effector_cartesian_velocity'][:3]
# We do not use angular velocity since it is very inaccurate in this environment
# eef_angular_vel = state['end_effector_cartesian_velocity'][3:]
# Concatenate the state
state['arm_concat'] = tf.concat([joint_pos, eef_pos, eef_ang, eef_vel], 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_joint_0_pos,gripper_joint_1_pos,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,eef_vel_x,eef_vel_y,eef_vel_z")
# 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 = 'jaco_play'
# 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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