177 citations · 724 across the 71 of their papers we have counts for
9 papers · 1 filter
Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks
Ziyun Wang, Volkan Isler, Daniel D. Lee
We address the problem of generating a high-resolution surface reconstruction from a single image. Our approach is to learn a Higher Order Function (HOF) which takes an image of an…
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
Nof Abuzainab, Tugba Erpek, Kemal Davaslioglu +8
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To…
Higher Order Function Networks for View Planning and Multi-View Reconstruction
Selim Engin, Eric Mitchell, Daewon Lee +2
We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D mod…
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Nicolai Häni, Pravakar Roy, Volkan Isler
In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant rece…
Asynchronous Network Formation in Unknown Unbounded Environments
Selim Engin, Volkan Isler
In this paper, we study the Online Network Formation Problem (ONFP) for a mobile multi-robot system. Consider a group of robots with a bounded communication range operating in a la…
Energy-efficient Path Planning for Ground Robots by Combining Air and Ground Measurements
Minghan Wei, Volkan Isler
As mobile robots find increasing use in outdoor applications, designing energy-efficient robot navigation algorithms is gaining importance. There are two primary approaches to ener…
Higher-Order Function Networks for Learning Composable 3D Object Representations
Eric Mitchell, Selim Engin, Volkan Isler +1
We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. T…
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner
Tarik Tosun, Eric Mitchell, Ben Eisner +6
We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of hu…
Semantics-Aware Image to Image Translation and Domain Transfer
Pravakar Roy, Nicolai Häni, Jun-Jee Chao +1
Image to image translation is the problem of transferring an image from a source domain to a different (but related) target domain. We present a new unsupervised image to image tra…