Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Benchmarking neural radiance fields for autonomous robots: An overview
Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …
Learning safe control for multi-robot systems: Methods, verification, and open challenges
In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Splat-nav: Safe real-time robot navigation in gaussian splatting maps
We present Splat-Nav, a real-time robot navigation pipeline for Gaussian Splatting (GSplat)
scenes, a powerful new 3D scene representation. Splat-Nav consists of two components: 1) …
scenes, a powerful new 3D scene representation. Splat-Nav consists of two components: 1) …
State-wise safe reinforcement learning with pixel observations
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the
challenges of balancing the tradeoff between maximizing rewards and minimizing safety …
challenges of balancing the tradeoff between maximizing rewards and minimizing safety …
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the" black box" nature of DRL agents …
agents that control autonomous systems. However, the" black box" nature of DRL agents …
Point cloud-based control barrier function regression for safe and efficient vision-based control
Control barrier functions have become an increasingly popular framework for safe real-time
control. In this work, we present a computationally low-cost framework for synthesizing …
control. In this work, we present a computationally low-cost framework for synthesizing …
Catnips: Collision avoidance through neural implicit probabilistic scenes
We introduce a transformation of a neural radiance field (NeRF) to an equivalent Poisson
point process (PPP). This PPP transformation allows for rigorous quantification of uncertainty …
point process (PPP). This PPP transformation allows for rigorous quantification of uncertainty …
Integrating neural radiance fields end-to-end for cognitive visuomotor navigation
Q Liu, H **n, Z Liu, H Wang - IEEE Transactions on Pattern …, 2024 - ieeexplore.ieee.org
We propose an end-to-end visuomotor navigation framework that leverages Neural
Radiance Fields (NeRF) for spatial cognition. To the best of our knowledge, this is the first …
Radiance Fields (NeRF) for spatial cognition. To the best of our knowledge, this is the first …