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About

Chau Do is a robotics researcher whose work focuses on enabling autonomous systems to master the complex, dynamic task of liquid manipulation. Her key research areas include robotic perception, manipulation, and reinforcement learning, with a particular emphasis on pouring—a fundamental skill for domestic and industrial service robots. Do’s major contributions lie in developing vision-based methods for accurate and spill-free pouring. Her most cited work, "A probabilistic approach to liquid level detection in cups using an RGB-D camera" (39 citations), pioneered a method for robots to detect and track liquid levels in real-time during a pour. She extended this in "Accurate Pouring with an Autonomous Robot Using an RGB-D Camera" (38 citations) and advanced the field further by applying deep reinforcement learning in "Learning to Pour using Deep Deterministic Policy Gradients" (28 citations), enabling robots to learn pouring policies that achieve high accuracy to specific heights without spilling. Collectively, her work has garnered over 100 citations, demonstrating significant impact in assistive robotics. By tackling the challenging physics of liquids, Do has helped pave the way for more capable and reliable robotic assistants in everyday environments.