1 code implementation • 25 Jan 2023 • James Watson, Nikolaus Correll
Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful.
no code implementations • 22 May 2022 • Sarah Aguasvivas Manzano, Vani Sundaram, Artemis Xu, Khoi Ly, Mark Rentschler, Robert Shepherd, Nikolaus Correll
We then study experimental results on two experimental rigs with different sensing, actuation and computational hardware: a tendon-based platform with embedded LightLace sensors and a HASEL-based platform with magnetic sensors.
no code implementations • 4 Jan 2021 • Sarah Aguasvivas Manzano, Patricia Xu, Khoi Ly, Robert Shepherd, Nikolaus Correll
We present a high-bandwidth, lightweight, and nonlinear output tracking technique for soft actuators that combines parsimonious recursive layers for forward output predictions and online optimization using Newton-Raphson.
no code implementations • 7 Feb 2020 • James Watson, Austin Miller, Nikolaus Correll
We present algorithms and results for a robotic manipulation system that was designed to be easily programmable and adaptable to various tasks common to industrial setting, which is inspired by the Industrial Assembly Challenge at the 2018 World Robotics Summit in Tokyo.
Robotics
no code implementations • 2 Dec 2019 • Sumeet Batra, John Klingner, Nikolaus Correll
We present a method to register individual members of a robotic swarm in an augmented reality display while showing relevant information about swarm dynamics to the user that would be otherwise hidden.
1 code implementation • 10 Nov 2019 • Sarah Aguasvivas Manzano, Dana Hughes, Cooper Simpson, Radhen Patel, Nikolaus Correll
We present a library to automatically embed signal processing and neural network predictions into the material robots are made of.
no code implementations • 11 Jun 2016 • Dana Hughes, Nikolaus Correll
As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered.
no code implementations • 21 Jan 2016 • Nikolaus Correll, Kostas E. Bekris, Dmitry Berenson, Oliver Brock, Albert Causo, Kris Hauser, Kei Okada, Alberto Rodriguez, Joseph M. Romano, Peter R. Wurman
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
no code implementations • 4 Nov 2014 • Lu Ma, Mahsa Ghafarianzadeh, Dave Coleman, Nikolaus Correll, Gabe Sibley
We present an unsupervised framework for simultaneous appearance-based object discovery, detection, tracking and reconstruction using RGBD cameras and a robot manipulator.