Search Results for author:
Found 10 papers, 3 papers with code
Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty
Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues.
Distributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth Control Barrier Functions
This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs).
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control
We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models.
Safe Reinforcement Learning Using Robust Control Barrier Functions
Reinforcement Learning (RL) has been shown to be effective in many scenarios.
Model-based Reinforcement Learning
reinforcement-learning
+2
Model Free Barrier Functions via Implicit Evading Maneuvers
This paper demonstrates that the safety override arising from the use of a barrier function can in some cases be needlessly restrictive.
Neural Identification for Control
The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law.
Constraint Learning for Control Tasks with Limited Duration Barrier Functions
When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration.
Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies
We propose a collection of RMPs for simple multi-robot tasks that can be used for building controllers for more complicated tasks.
Robotics
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics.
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process.
