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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
action_dict = step['action_dict']
# Robot action
eef_pos = action_dict['cartesian_position'][:3]
eef_ang = action_dict['cartesian_position'][3:6]
eef_ang = euler_to_rotation_matrix(eef_ang)
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
eef_pos_vel = action_dict['cartesian_velocity'][:3]
eef_ang_vel = action_dict['cartesian_velocity'][3:6]
joint_pos = action_dict['joint_position']
joint_vel = action_dict['joint_velocity']
grip_pos = action_dict['gripper_position']
grip_vel = action_dict['gripper_velocity']
# Concatenate the action
step['action'] = {}
action = step['action']
arm_action = tf.concat([eef_pos, eef_ang, eef_pos_vel, eef_ang_vel, joint_pos, joint_vel, grip_pos, grip_vel], axis=0)
action['arm_concat'] = arm_action
action['terminate'] = step['is_terminal']
# Write the action format
action['format'] = tf.constant(
"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,eef_angular_vel_roll,eef_angular_vel_pitch,eef_angular_vel_yaw,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,gripper_joint_0_pos,gripper_joint_0_vel")
# Convert raw state to our state
# Robot state
state = step['observation']
eef_pos = state['cartesian_position'][:3]
eef_ang = state['cartesian_position'][3:6]
eef_ang = euler_to_rotation_matrix(eef_ang)
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
joint_pos = state['joint_position']
grip_pos = 1 - state['gripper_position']
# Concatenate the state
state['arm_concat'] = tf.concat([
joint_pos,grip_pos,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,gripper_joint_0_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")
# 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__":
pass
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