Generating realistic simulations is critical for autonomous system applications such as self-
driving and human-robot interactions. However, driving simulators nowadays still have …

We present a verification-based learning framework VEL that synthesizes safe programmatic
controllers for environments with continuous state and action spaces. The key idea is the …

Ensuring safety and meeting temporal specifications are critical challenges for long-term
robotic tasks. Signal temporal logic (STL) has been widely used to systematically and …

Before autonomous systems can be deployed in safety-critical applications, we must be able
to understand and verify the safety of these systems. For cases where the risk or cost of real …

Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a
fixed dataset. Current state-of-the-art approaches are based on supervised learning with a …

This article considers the multiagent control problem under global temporal logic tasks,
where agents possess heterogeneous capabilities. The global temporal logic task imposes …

Before autonomous systems can be deployed in safety-critical applications, we must be able
to understand and verify the safety of these systems. For cases where the risk or cost of real …

Signal Temporal Logic (STL) is a powerful specification language for describing complex
temporal behaviors of continuous signals, making it well-suited for high-level robotic task …

Prediction of failures in real-world robotic systems either requires accurate model
information or extensive testing. Partial knowledge of the system model makes simulation …

Synthesizing planning and control policies in robotics is a fundamental task, further
complicated by factors such as complex logic specifications and high-dimensional robot …