3 accepted papers
In meta-learning and its downstream tasks, many methods use implicit adaptation to represent task-specific variations. However, implicit approaches hinder interpretability and make it difficult to understand which task factors drive performance. In this work, we introduce a disentangled multi-contex…
In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solvin…
Neural networks have been increasingly employed in Model Predictive Controller (MPC) to control nonlinear dynamic systems. However, MPC still poses a problem that an achievable update rate is insufficient to cope with model uncertainty and external disturbances. In this letter, we present a novel co