Disturbances are inevitably found in almost every system and, if not rejected, they could
jeopardize the effectiveness of control methods. Thereby, employing state-of-the-art …

Safety is a critical component of autonomous systems and remains a challenge for learning-
based policies to be utilized in the real world. In particular, policies learned using …

With the recent development of deep learning, AI models are widely used in various
domains. AI models show good performance for definite tasks such as image classification …

Safety is one of the biggest concerns to applying reinforcement learning (RL) to the physical
world. In its core part, it is challenging to ensure RL agents persistently satisfy a hard state …

This letter presents a memory-augmented system identifier with finite-time convergence for
continuous-time uncertain nonlinear systems. A memory of events with significant effect on …

Safety is an important topic in autonomous driving since any collision may cause serious
injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal …

Solving high-dimensional optimal control problems in real-time is an important but
challenging problem, with applications to multiagent path planning problems, which have …

Solving high-dimensional optimal control problems and corresponding Hamilton–Jacobi
PDEs are important but challenging problems in control engineering. In this paper, we …

Visual navigation requires the agent reasonably perceives the environment and effectively
navigates to the given target. In this task, we present a Multimodal Adaptive Graph (MAG) for …

This paper investigates a hybrid solution which combines deep reinforcement learning (RL)
and classical trajectory planning for the" following in front" application. Here, an autonomous …