93 citations · 210 across the 9 of their papers we have counts for
10 papers
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Andras Kupcsik, Markus Spies, Alexander Klein +4
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suit…
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Leonel Rozo, Meng Guo, Andras G. Kupcsik +8
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current works…
Smart Grid State Estimation with PMUs Time Synchronization Errors
Marco Todescato, Ruggero Carli, Luca Schenato +1
We consider the problem of PMU-based state estimation combining information coming from ubiquitous power demand time series and only a limited number of PMUs. Conversely to recent…
Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
Nicola Bastianello, Ruggero Carli, Luca Schenato +1
In this work we focus on the problem of minimizing the sum of convex cost functions in a distributed fashion over a peer-to-peer network. In particular, we are interested in the ca…
A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks
Nicola Bastianello, Marco Todescato, Ruggero Carli +1
In this paper we propose a distributed implementation of the relaxed Alternating Direction Method of Multipliers algorithm (R-ADMM) for optimization of a separable convex cost func…
Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach
Nicola Bastianello, Marco Todescato, Ruggero Carli +1
In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Buildin…
Generalized gradient optimization over lossy networks for partition-based estimation
Marco Todescato, Nicoletta Bof, Guido Cavraro +2
We address the problem of distributed convex unconstrained optimization over networks characterized by asynchronous and possibly lossy communications. We analyze the case where the…
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
Marco Todescato, Andrea Carron, Ruggero Carli +2
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly app…
Voltage stress minimization by optimal reactive power control
Marco Todescato, John W. Simpson-Porco, Florian Dörfler +2
A standard operational requirement in power systems is that the voltage magnitudes lie within prespecified bounds. Conventional engineering wisdom suggests that such a tightly-regu…
Multi-agents adaptive estimation and coverage control using Gaussian regression
Andrea Carron, Marco Todescato, Ruggero Carli +2
We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitio…