Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

This article proposes the novel concepts of the high-order discrete-time control barrier
function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to …

This letter addresses the synthesis of safety-critical controllers using estimate feedback. We
propose an observer-controller interconnection to ensure that the nonlinear system remains …

We study the problem of synthesizing a control strategy to enforce safety of affine-in-control
stochastic dynamical systems over finite time horizons. We use stochastic control barrier …

Robots deployed in unstructured, real-world environments operate under considerable
uncertainty due to imperfect state estimates, model error, and disturbances. Given this real …

We provide a new approach to synthesize controllers for nonlinear continuous dynamical
systems with control against safety properties. The controllers are based on neural networks …

Enforcing safety in the presence of stochastic uncertainty is a challenging problem.
Traditionally, researchers have proposed safety in the statistical mean as a safety measure …

This article presents a layered control approach for real-time trajectory planning and control
of robust cooperative locomotion by two holonomically constrained quadrupedal robots. A …

This paper considers the general problem of transitioning theoretically safe controllers to
hardware. Concretely, we explore the application of control barrier functions (CBFs) to …