activity
20122023
most citedA distributed primal-dual interior-point method for loosely coupled problems using ADMM

9 citations · 22 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SY20233 cited

A Biologically-Inspired Computational Model of Time Perception

Inês Lourenço, Robert Mattila, Rodrigo Ventura +1

Time perception - how humans and animals perceive the passage of time - forms the basis for important cognitive skills such as decision-making, planning, and communication. In this…

cs.RO2023

Diagnosing and Augmenting Feature Representations in Correctional Inverse Reinforcement Learning

Inês Lourenço, Andreea Bobu, Cristian R. Rojas +1

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned…

cs.LG2023

Optimal Transport for Correctional Learning

Rebecka Winqvist, Inês Lourenco, Francesco Quinzan +2

The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to ano…

eess.SY20231 cited

Prediction-Based Leader-Follower Rendezvous Model Predictive Control with Robustness to Communication Losses

Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas +1

In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs…

cs.RO2023

Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control

Rui Oliveira, Siddharth H. Nair, Bo Wahlberg

Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-mo…

math.OC20149 cited

A distributed primal-dual interior-point method for loosely coupled problems using ADMM

Mariette Annergren, Sina Khoshfetrat Pakazad, Anders Hansson +1

In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method…