Lifelong Learning-Based Adaptive Motion Planning for Field Deployment Operations with a Quadruped Robot

This project aims to develop a learning-based novel locomotion controller that enables quadruped robots to adapt online to diverse and unpredictable terrain conditions during operation. Going beyond the classical train & deploy paradigm, the approach leverages continuous learning in locomotion control, allowing the robot to draw on past experiences and adjust to new conditions in real time. By fusing visual data and onboard sensory measurements with ground impedance information, the learning process is enriched, while seamless integration of the algorithm into a stable locomotion controller ensures that learning remains dynamic and continuous in the field. In this way, robots gain the ability to adapt to challenging terrain conditions throughout operations, paving the way for them to become active collaborators with humans in outdoor environments.