Zobrazeno 1 - 10
of 245
pro vyhledávání: '"Hall, Adam P."'
Autor:
Zhou, Siqi, Brunke, Lukas, Tao, Allen, Hall, Adam W., Bejarano, Federico Pizarro, Panerati, Jacopo, Schoellig, Angela P.
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science develop more advanced algorithms, we become more dependent on complex com
Externí odkaz:
http://arxiv.org/abs/2308.10008
Publikováno v:
in IEEE Control Systems Letters, vol. 7, pp. 2191-2196, 2023
Learning-based optimal control algorithms control unknown systems using past trajectory data and a learned model of the system dynamics. These controllers use either a linear approximation of the learned dynamics, trading performance for faster compu
Externí odkaz:
http://arxiv.org/abs/2307.10541
Autor:
Yuan, Zhaocong, Hall, Adam W., Zhou, Siqi, Brunke, Lukas, Greeff, Melissa, Panerati, Jacopo, Schoellig, Angela P.
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained significant traction. However, to adequately gauge the progress and a
Externí odkaz:
http://arxiv.org/abs/2109.06325
Autor:
Marić, Filip, Giamou, Matthew, Hall, Adam W., Khoubyarian, Soroush, Petrović, Ivan, Kelly, Jonathan
Publikováno v:
IEEE Transactions on Robotics (T-RO), Vol. 38, No. 3, pp. 1703-1722, Jun. 2022
Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping betw
Externí odkaz:
http://arxiv.org/abs/2108.13720
Autor:
Brunke, Lukas, Greeff, Melissa, Hall, Adam W., Yuan, Zhaocong, Zhou, Siqi, Panerati, Jacopo, Schoellig, Angela P.
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of
Externí odkaz:
http://arxiv.org/abs/2108.06266
Autor:
Hall, Adam James, Jay, Madhava, Cebere, Tudor, Cebere, Bogdan, van der Veen, Koen Lennart, Muraru, George, Xu, Tongye, Cason, Patrick, Abramson, William, Benaissa, Ayoub, Shah, Chinmay, Aboudib, Alan, Ryffel, Théo, Prakash, Kritika, Titcombe, Tom, Khare, Varun Kumar, Shang, Maddie, Junior, Ionesio, Gupta, Animesh, Paumier, Jason, Kang, Nahua, Manannikov, Vova, Trask, Andrew
We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of
Externí odkaz:
http://arxiv.org/abs/2104.12385
We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrate that the attack can be successfully performed with limited knowledge
Externí odkaz:
http://arxiv.org/abs/2104.05743
Autor:
Romanini, Daniele, Hall, Adam James, Papadopoulos, Pavlos, Titcombe, Tom, Ismail, Abbas, Cebere, Tudor, Sandmann, Robert, Roehm, Robin, Hoeh, Michael A.
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keep
Externí odkaz:
http://arxiv.org/abs/2104.00489
Autor:
Papadopoulos, Pavlos, Abramson, Will, Hall, Adam J., Pitropakis, Nikolaos, Buchanan, William J.
Publikováno v:
Mach. Learn. Knowl. Extr. 2021, 3(2), 333-356
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited
Externí odkaz:
http://arxiv.org/abs/2103.15753
Autor:
Hall, Adam
We discuss the combining of measurements where single measurement covariances are given but the joint measurement covariance is unknown. For this paper we assume the mapping of a single measurement to the solution space is the identity matrix. We exa
Externí odkaz:
http://arxiv.org/abs/2101.12034