41 citations · 211 across the 45 of their papers we have counts for
45 papers
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…
Kinodynamic Motion Planning via Funnel Control for Underactuated Unmanned Surface Vehicles
Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas +1
We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion p…
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…
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…
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…
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…
Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance
Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas +1
We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadrotor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of…
A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
Inês Lourenço, Rebecka Winqvist, Cristian R. Rojas +1
A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest. Correctional…
Learning Models of Model Predictive Controllers using Gradient Data
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
This paper investigates controller identification given data from a Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses…
Aperiodic Communication for MPC in Autonomous Cooperative Landing
Dženan Lapandić, Linnea Persson, Dimos V. Dimarogonas +1
This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous a…
Cooperative System Identification via Correctional Learning
Inês Lourenço, Robert Mattila, Cristian R. Rojas +1
We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-ag…
A Geometric Approach to On-road Motion Planning for Long and Multi-Body Heavy-Duty Vehicles
Rui Oliveira, Oskar Ljungqvist, Pedro F. Lima +2
Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicl…
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
Lucas N. Egidio, Anders Hansson, Bo Wahlberg
We consider the problem of how to learn a step-size policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. This is a limited computational memory quasi-…
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Arun Venkitaraman, Anders Hansson, Bo Wahlberg
This paper investigates the use of nonparametric kernel-regression to obtain a tasksimilarity aware meta-learning algorithm. Our hypothesis is that the use of tasksimilarity helps…
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have propo…
How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making
Inês Lourenço, Robert Mattila, Cristian R. Rojas +1
We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filte…
Inverse Filtering for Hidden Markov Models with Applications to Counter-Adversarial Autonomous Systems
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in…
Optimization-Based On-Road Path Planning for Articulated Vehicles
Rui Oliveira, Oskar Ljungqvist, Pedro F. Lima +1
Maneuvering an articulated vehicle on narrow road stretches is often a challenging task for a human driver. Unless the vehicle is accurately steered, parts of the vehicle's bodies…
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)
Inês Lourenço, Bo Wahlberg, Rodrigo Ventura
Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots…
Learning sparse linear dynamic networks in a hyper-parameter free setting
Arun Venkitaraman, Håkan Hjalmarsson, Bo Wahlberg
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamic…
Recursive Prediction of Graph Signals with Incoming Nodes
Arun Venkitaraman, Saikat Chatterjee, Bo Wahlberg
Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many rea…
What Did Your Adversary Believe? Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems
Robert Mattila, Inês Lourenço, Vikram Krishnamurthy +2
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our curre…
Path Planning for Autonomous Bus Driving in Urban Environments
Rui Oliveira, Pedro F. Lima, Gonçalo Collares Pereira +2
Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large…
Algorithms and Performance Analysis for Stochastic Wiener System Identification
Bo Wahlberg, Lennart Ljung
We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear d…