  Imitation Learning (IL) has emerged as a powerful approach in robotics,
allowing robots to acquire new skills by mimicking human actions. Despite its
potential, the data collection process for IL remains a significant challenge
due to the logistical difficulties and high costs associated with obtaining
high-quality demonstrations. To address these issues, we propose a low-cost
visual teleoperation system for bimanual manipulation tasks, called VITAL. Our
approach leverages affordable hardware and visual processing techniques to
collect demonstrations, which are then augmented to create extensive training
datasets for imitation learning. We enhance the generalizability and robustness
of the learned policies by utilizing both real and simulated environments and
human-in-the-loop corrections. We evaluated our method through several rounds
of experiments in simulated and real-robot settings, focusing on tasks of
varying complexity, including bottle collecting, stacking objects, and
hammering. Our experimental results validate the effectiveness of our approach
in learning robust robot policies from simulated data, significantly improved
by human-in-the-loop corrections and real-world data integration. Additionally,
we demonstrate the framework's capability to generalize to new tasks, such as
setting a drink tray, showcasing its adaptability and potential for handling a
wide range of real-world bimanual manipulation tasks. A video of the
experiments can be found at: https://youtu.be/YeVAMRqRe64?si=R179xDlEGc7nPu8i
