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import tensorflow as tf
from data.utils import clean_task_instruction, euler_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
arm_action = step['action']
# Concatenate the action
step['action'] = {}
action = step['action']
action['arm_concat'] = arm_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,gripper_open")
action['terminate'] = step['is_terminal']
# The state has any problem?
state = step['observation']
eef_pos = state['xyz']
# Clip eef_pos to be [-10, 10] for stability
eef_pos = tf.clip_by_value(eef_pos, -10, 10)
eef_ang = state['rot']
eef_ang = euler_to_rotation_matrix(eef_ang)
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
grip_pos = state['gripper']
# Concatenate the state
state['arm_concat'] = tf.concat([
grip_pos,eef_pos,eef_ang], axis=0)
# Write the state format
state['format'] = tf.constant(
"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
from tqdm import tqdm
import numpy as np
DATASET_DIR = 'data/datasets/openx_embod'
DATASET_NAME = 'dobbe'
# Load the dataset
dataset = tfds.builder_from_directory(
builder_dir=dataset_to_path(
DATASET_NAME, DATASET_DIR))
dataset = dataset.as_dataset(split='all')
# dataset = dataset.filter(
# lambda x: tf.math.less(
# tf.math.reduce_max(tf.math.abs(
# tf.convert_to_tensor(
# [step['observation']['xyz'] for step in x['steps']]))), 3))
# Inspect the dataset
for i, episode in tqdm(enumerate(dataset), total=5208):
res = []
for step in episode['steps']:
res.append(step['observation']['xyz'].numpy())
max_val = np.max(np.abs(res))
if max_val > 2:
print(f"Episode {i} has a max value of {max_val}")
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