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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.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']
# Robot action, consists of [3x end-effector position residual, 3x end-effector axis-angle residual,
# 7x robot joint k_p gain coefficient, 7x robot joint damping ratio coefficient].
# The action residuals are global, i.e. multiplied on theleft-hand side of the current end-effector state.
eef_delta_pos = action[:3]
eef_ang = action[3:6]
eef_ang = euler_to_quaternion(eef_ang)
# Concatenate the action
step['action'] = {}
action = step['action']
action['terminate'] = step['is_terminal']
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']
# Robot state, consists of [joint_states, end_effector_pose].Joint states are 14-dimensional, formatted in the order of [q_0, w_0, q_1, w_0, ...].
# In other words, joint positions and velocities are interleaved.The end-effector pose is 7-dimensional, formatted in the order of [position, quaternion].The quaternion is formatted in (x,y,z,w) order. The end-effector pose references the tool frame, in the center of the two fingers of the gripper.
joint_states = state['state'][:14]
arm_joint_pos = joint_states[::2]
arm_joint_vel = joint_states[1::2]
eef_pos = state['state'][14:17]
# eef_ang = quaternion_to_euler(state['state'][17:21])
eef_ang = quaternion_to_rotation_matrix(state['state'][17:21])
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
# Concatenate the state
state['arm_concat'] = tf.concat([arm_joint_pos, arm_joint_vel, 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,arm_joint_6_pos,arm_joint_0_vel,arm_joint_1_vel,arm_joint_2_vel,arm_joint_3_vel,arm_joint_4_vel,arm_joint_5_vel,arm_joint_6_vel,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 = 'fractal20220817_data'
# 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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