Stanford Seminar - Learning-enabled Adaptation to Evolving Conditions for Robotics

May 3, 2024
Somrita Banerjee, Stanford University

With advancements in machine learning and artificial intelligence, a new generation of “learning-enabled” robots is emerging, which are better suited to operating autonomously in unstructured, uncertain, and unforgiving environments. To achieve these goals, robots must be able to adapt to evolving conditions that are different from those seen during training or expected during deployment. In this talk I will first talk about adapting to novel instantiations, i.e., different task instances with shared structure, through parameter adaptation. Such adaptation is done passively, by augmenting physics-based models with learned models, with our key contribution being that the interpretability of physical parameters is retained, allowing us to monitor adaptation. Second, I will talk about a framework for active adaptation where the model monitors its own performance and curates a diverse subset of uncertain inputs to be used for periodic fine-tuning of the model, improving performance over the full data lifecycle.