Ocean World Lander Autonomy Testbed

Ocean World Lander Autonomy Testbeds (OWLATs) are simulated and physical environments designed to develop and test the autonomous capabilities of robotic landers for extraterrestrial exploration, focusing on tasks like granular material sampling and safe landing. Current research emphasizes robust machine learning techniques, including deep Gaussian processes and deep reinforcement learning, to enable adaptive behavior in unpredictable terrains and under varying gravitational conditions, often incorporating physics-based simulation to improve model accuracy and generalization. These advancements are crucial for enabling future autonomous missions, improving the reliability and efficiency of sample collection, and enhancing the safety of landing procedures on celestial bodies.