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About

Manasa Mainampati is a robotics researcher whose work sits at the intersection of autonomous navigation, human-robot interaction, and reinforcement learning. Her primary research focuses on enabling mobile robots—particularly the TurtleBot platform—to explore unknown environments, build maps, and navigate autonomously using frontier-based exploration and path-planning algorithms like A*. A key contribution is her pioneering integration of Human-in-the-Loop (HITL) techniques with reinforcement learning methods, including Q-learning, SARSA, and Soft Actor-Critic (SAC), to improve robot obstacle avoidance and decision-making in indoor settings. Her most cited work, “Autonomous Exploring Map and Navigation for an Agricultural Robot” (25 citations), demonstrates the practical application of these algorithms in agricultural robotics. Through papers like “Implementation of Human in The Loop on the TurtleBot using Reinforced Learning methods” and “A Human in the Loop Based Robotic System by Using Soft Actor Critic,” she has advanced the field of shared autonomy, showing how human feedback can enhance machine learning for safer, more adaptive robotic systems. Her cumulative work, spanning both hardware and software integration, provides a strong foundation for researchers interested in accessible, learning-based robotics.