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import tensorflow as tf
from data.utils import clean_task_instruction, quaternion_to_euler, euler_to_quaternion
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 delta target position, 3x end effector delta target orientation in axis-angle format, 1x gripper target position (mimic for two fingers)].
# For delta target position, an action of -1 maps to a robot movement of -0.1m, and action of 1 maps to a movement of 0.1m.
# For delta target orientation, its encoded angle is mapped to a range of [-0.1rad, 0.1rad] for robot execution. For example, an action of [1, 0, 0] means rotating along the x-axis by 0.1 rad.
# For gripper target position, an action of -1 means close, and an action of 1 means open.
eef_delta_pos = action[:3] * 0.1
eef_ang = action[3:6] * 0.1
eef_ang = euler_to_quaternion(eef_ang)
grip_open = tf.expand_dims((action[6] + 1) / 2, axis=0)
# Concatenate the action
step['action'] = {}
action = step['action']
arm_action = tf.concat([eef_delta_pos, eef_ang, grip_open], axis=0)
action['arm_concat'] = arm_action
action['terminate'] = step['is_terminal']
# 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,gripper_open")
# Convert raw state to our state
# Robot state, consists of [7x robot joint angles, 2x gripper position, 7x robot joint angle velocity, 2x gripper velocity]. Angle in radians, position in meters.
state = step['observation']['state']
arm_joint_pos = state[:7]
gripper_pos = state[7:9] * 25 # rescale to [0, 1]
# We do not use velocity since it is very inaccurate in this environment
# arm_joint_vel = state[9:16]
# gripper_vel = state[16:18]
# Concatenate the state
state = step['observation']
state['arm_concat'] = tf.concat([
arm_joint_pos, gripper_pos], axis=0)
# Robot base pose in the world frame, consists of [x, y, z, qw, qx, qy, qz].
# The first three dimensions represent xyz positions in meters. The last four dimensions are the quaternion representation of rotation
# base_pose = step['observation']['base_pose']
# base_pose_xyz = base_pose[:3]
# base_pose_ang = quaternion_to_euler(base_pose[3:])
# processed_base_pose = tf.concat([base_pose_xyz, base_pose_ang], axis=0)
# state['arm_concat'] = tf.concat([state['arm_concat'], processed_base_pose], 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,gripper_joint_0_pos,gripper_joint_1_pos")
# 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 = 'maniskill_dataset_converted_externally_to_rlds'
# 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.take(1):
for step in episode['steps']:
print(step['is_last'])
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