| | 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. |
| | """ |
| | |
| | action_dict = step['action'] |
| | |
| | step['action'] = {} |
| | action = step['action'] |
| | action['arm_concat'] = action_dict['ee_6d_pos'] |
| |
|
| | |
| | action['format'] = tf.constant( |
| | "left_eef_pos_x,left_eef_pos_y,left_eef_pos_z,left_eef_angle_0,left_eef_angle_1,left_eef_angle_2,left_eef_angle_3,left_eef_angle_4,left_eef_angle_5,left_gripper_open,right_eef_pos_x,right_eef_pos_y,right_eef_pos_z,right_eef_angle_0,right_eef_angle_1,right_eef_angle_2,right_eef_angle_3,right_eef_angle_4,right_eef_angle_5,right_gripper_open" |
| | ) |
| |
|
| | |
| | |
| | state_dict = step['observation']['state'] |
| | state = {} |
| | state['arm_concat'] = state_dict |
| |
|
| | |
| | state['format'] = tf.constant( |
| | "left_eef_pos_x,left_eef_pos_y,left_eef_pos_z,left_eef_angle_0,left_eef_angle_1,left_eef_angle_2,left_eef_angle_3,left_eef_angle_4,left_eef_angle_5,left_gripper_open,right_eef_pos_x,right_eef_pos_y,right_eef_pos_z,right_eef_angle_0,right_eef_angle_1,right_eef_angle_2,right_eef_angle_3,right_eef_angle_4,right_eef_angle_5,right_gripper_open" |
| | ) |
| | |
| | |
| | replacements = { |
| | '_': ' ', |
| | '1f': ' ', |
| | '4f': ' ', |
| | '-': ' ', |
| | '50': ' ', |
| | '55': ' ', |
| | '56': ' ', |
| | |
| | } |
| | instr = step['language_instruction'] |
| | |
| | step['observation'] = state |
| | step['observation']['natural_language_instruction'] = instr |
| |
|
| | return step |
| |
|
| |
|
| | if __name__ == "__main__": |
| | pass |
| |
|