We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

This paper proposes an algorithm for motion planning among dynamic agents using
adaptive conformal prediction. We consider a deterministic control system and use trajectory …

Trajectory prediction is an integral component of modern autonomous systems as it allows
for envisioning future intentions of nearby moving agents. Due to the lack of other agents' …

The control of dynamical systems under temporal logic specifications among uncontrollable
dynamic agents is challenging due to the agents'a-priori unknown behavior. Existing works …

Several task and motion planning algorithms have been proposed recently to design paths
for mobile robot teams with collaborative high-level missions specified using formal …

Multi-robot collaboration for target tracking presents significant challenges in hazardous
environments, including addressing robot failures, dynamic priority changes, and other …

Formal languages effectively outline robots' task specifications, yet current temporal logic
struggles to balance semantic expression with solution speed. To address this challenge …

This paper introduces an innovative deep learning framework for robotic path planning. This
framework addresses two fundamental challenges:(1) integration of mission specifications …

In this paper, we consider teams of robots with heterogeneous skills (eg, sensing and
manipulation) tasked with collaborative missions described by Linear Temporal Logic (LTL) …