101 citations · 286 across the 30 of their papers we have counts for
13 papers · 1 filter
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Tim Seyde, Igor Gilitschenski, Wilko Schwarting +4
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known…
Multi-Objective Graph Heuristic Search for Terrestrial Robot Design
Jie Xu, Andrew Spielberg, Allan Zhao +2
We present methods for co-designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single-…
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
Tixiao Shan, Brendan Englot, Carlo Ratti +1
We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high ac…
Efficient and Robust LiDAR-Based End-to-End Navigation
Zhijian Liu, Alexander Amini, Sibo Zhu +3
Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably accurate i…
Estimating the State of Epidemics Spreading with Graph Neural Networks
Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro +2
When an epidemic spreads into a population, it is often unpractical or impossible to have a continuous monitoring of all subjects involved. As an alternative, algorithmic solutions…
Feedback from Pixels: Output Regulation via Learning-Based Scene View Synthesis
Murad Abu-Khalaf, Sertac Karaman, Daniela Rus
We propose a novel controller synthesis involving feedback from pixels, whereby the measurement is a high dimensional signal representing a pixelated image with Red-Green-Blue (RGB…