File size: 3,253 Bytes
16628c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
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']
# Robot action, consists of [7 delta joint pos,1x gripper binary state].
delta_joint_pos = action[:7]
grip_open = tf.expand_dims(1 - action[7], axis=0)
# Concatenate the action
# action['arm_concat'] = tf.concat([eef_delta_pos, eef_ang, grip_open], axis=0)
step['action'] = {}
action = step['action']
action['arm_concat'] = tf.concat([delta_joint_pos, grip_open], axis=0)
action['terminate'] = step['is_terminal']
# Write the action format
action['format'] = tf.constant(
"arm_joint_0_delta_pos,arm_joint_1_delta_pos,arm_joint_2_delta_pos,arm_joint_3_delta_pos,arm_joint_4_delta_pos,arm_joint_5_delta_pos,arm_joint_6_delta_pos,gripper_open")
# Convert raw state to our state
state = step['observation']
# xArm joint positions (7 DoF).
arm_joint_pos = state['joint_pos']
# Binary gripper state (1 - closed, 0 - open)
grip_open = tf.expand_dims(1 - tf.cast(state['gripper'],dtype=tf.float32), axis=0)
# Gripper pose, robot frame, [3 position, 4 rotation]
gripper_pose = state['pose'][:3]
# gripper_ang = quaternion_to_euler(state['pose'][3:7])
gripper_ang = quaternion_to_rotation_matrix(state['pose'][3:7])
gripper_ang = rotation_matrix_to_ortho6d(gripper_ang)
# Concatenate the state
state['arm_concat'] = tf.concat([arm_joint_pos, gripper_pose,gripper_ang, grip_open], 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,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,gripper_open")
# 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)
|