How to download BAIR action free robot pushing dataset?
BAIR dataset can be downloaded here
https://sites.google.com/berkeley.edu/robotic-interaction-datasets

Additionally, here is the code to extract data from the dataset

import datetime
import os
import time

import cv2
import numpy as np
import skvideo.io
import tensorflow as tf
from PIL import Image
from tensorflow.python.platform import gfile

def get_next_video_data(data_dir):
    filenames = gfile.Glob(os.path.join(data_dir, '*'))
    if not filenames:
        raise RuntimeError('No data files found.')

    for f in filenames:
        k = 0
        for serialized_example in tf.python_io.tf_record_iterator(f):
            example = tf.train.Example()
            example.ParseFromString(serialized_example)
            # print(example)        # To know what all features are present

            actions = np.empty((0, 4), dtype='float')
            endeffector_positions = np.empty((0, 3), dtype='float')
            frames_aux1 = []
            frames_main = []
            i = 0
            while True:
                action_name = str(i) + '/action'
                action_value = np.array(example.features.feature[action_name].float_list.value)
                if action_value.shape == (0,):      # End of frames/data
                    break
                actions = np.vstack((actions, action_value))

                endeffector_pos_name = str(i) + '/endeffector_pos'
                endeffector_pos_value = list(example.features.feature[endeffector_pos_name].float_list.value)
                endeffector_positions = np.vstack((endeffector_positions, endeffector_pos_value))

                aux1_image_name = str(i) + '/image_aux1/encoded'
                aux1_byte_str = example.features.feature[aux1_image_name].bytes_list.value[0]
                aux1_img = Image.frombytes('RGB', (64, 64), aux1_byte_str)
                aux1_arr = np.array(aux1_img.getdata()).reshape((aux1_img.size[1], aux1_img.size[0], 3))
                frames_aux1.append(aux1_arr.reshape(1, 64, 64, 3))

                main_image_name = str(i) + '/image_main/encoded'
                main_byte_str = example.features.feature[main_image_name].bytes_list.value[0]
                main_img = Image.frombytes('RGB', (64, 64), main_byte_str)
                main_arr = np.array(main_img.getdata()).reshape((main_img.size[1], main_img.size[0], 3))
                frames_main.append(main_arr.reshape(1, 64, 64, 3))
                i += 1

            np_frames_aux1 = np.concatenate(frames_aux1, axis=0)
            np_frames_main = np.concatenate(frames_main, axis=0)
            yield f, k, actions, endeffector_positions, np_frames_aux1, np_frames_main
            k = k + 1


def extract_data(data_dir, output_dir, frame_rate):
    """
    Extracts data in tfrecord format to gifs, frames and text files
    :param data_dir:
    :param output_dir:
    :param frame_rate:
    :return:
    """
    if os.path.exists(output_dir):
        if os.listdir(output_dir):
            raise RuntimeError('Directory not empty: {0}'.format(output_dir))
    else:
        os.makedirs(output_dir)

    seq_generator = get_next_video_data(data_dir)
    while True:
        try:
            _, k, actions, endeff_pos, aux1_frames, main_frames = next(seq_generator)
        except StopIteration:
            break
        video_out_dir = os.path.join(output_dir, '{0:03}'.format(k))
        os.makedirs(video_out_dir)

        # noinspection PyTypeChecker
        np.savetxt(os.path.join(video_out_dir, 'actions.csv'), actions, delimiter=',')
        # noinspection PyTypeChecker
        np.savetxt(os.path.join(video_out_dir, 'endeffector_positions.csv'), endeff_pos, delimiter=',')
        skvideo.io.vwrite(os.path.join(video_out_dir, 'aux1.gif'), aux1_frames, inputdict={'-r': str(frame_rate)})
        skvideo.io.vwrite(os.path.join(video_out_dir, 'main.gif'), main_frames, inputdict={'-r': str(frame_rate)})
        skvideo.io.vwrite(os.path.join(video_out_dir, 'aux1.mp4'), aux1_frames, inputdict={'-r': str(frame_rate)})
        skvideo.io.vwrite(os.path.join(video_out_dir, 'main.mp4'), main_frames, inputdict={'-r': str(frame_rate)})

        # Save frames
        aux1_folder_path = os.path.join(video_out_dir, 'aux1_frames')
        os.makedirs(aux1_folder_path)
        for i, frame in enumerate(aux1_frames):
            filepath = os.path.join(aux1_folder_path, 'frame_{0:03}.bmp'.format(i))
            cv2.imwrite(filepath, cv2.cvtColor(frame.astype('uint8'), cv2.COLOR_RGB2BGR))
        main_folder_path = os.path.join(video_out_dir, 'main_frames')
        os.makedirs(main_folder_path)
        for i, frame in enumerate(main_frames):
            filepath = os.path.join(main_folder_path, 'frame_{0:03}.bmp'.format(i))
            cv2.imwrite(filepath, cv2.cvtColor(frame.astype('uint8'), cv2.COLOR_RGB2BGR))
        print('Saved video: {0:03}'.format(k))


def main():
    data_dir = '../softmotion30_44k/test'
    output_dir = '../ExtractedData/test'
    frame_rate = 4
    extract_data(data_dir, output_dir, frame_rate)
    return


if __name__ == '__main__':
    print('Program started at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
    start_time = time.time()
    main()
    end_time = time.time()
    print('Program ended at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
    print('Execution time: ' + str(datetime.timedelta(seconds=end_time - start_time)))


References:
https://github.com/edenton/svg/blob/master/data/convert_bair.py