Author name cluster

Luis Montesano

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

21 papers

2 author rows

IROS Conference 2020 Conference Paper

Robust and efficient post-processing for video object detection

Alberto Sabater
Luis Montesano
Ana C. Murillo

Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of stat-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.

IROS Conference 2015 Conference Paper

Learning multiple behaviours using hierarchical clustering of rewards

Javier Almingol
Luis Montesano

Learning models of behaviours has many applications in robotics spanning both control, e. g. learning from demonstration and perception, e. g. monitoring and surveillance. Inverse reinforcement learning encodes behaviours as a reward function learned from a set of demonstrations. This paper addresses the problem of learning from unlabelled datasets containing an unknown number of behaviours in continuous action-state spaces. The proposed method uses a hierarchical clustering approach to directly group trajectories that share a common reward function. The similarity metric is based on the distribution of maximum entropy of the feature counts computed using path integrals. We evaluated the method in three different tasks: navigation on a set of synthetic maps, human driving styles on a simulator and human reaching. Results show that clustering in the reward space is able to discover the latent reward structure resulting in compact models that can generate all the observed behaviours.

AAAI Conference 2014 Conference Paper

Calibration-Free BCI Based Control

Jonathan Grizou
Iñaki Iturrate
Luis Montesano
Pierre-Yves Oudeyer
Manuel Lopes

Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.

UAI Conference 2014 Conference Paper

Interactive Learning from Unlabeled Instructions

Jonathan Grizou
Iñaki Iturrate
Luis Montesano
Pierre-Yves Oudeyer
Manuel Lopes 0001

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practical application since the user can start controlling a device from scratch, without the need of an expert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruction meanings and the possible tasks. Furthermore, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.

ICML Conference 2013 Conference Paper

Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space

Javier Almingol
Luis Montesano
Manuel Lopes 0001

In this paper we introduce a method to learn multiple behaviors in the form of motor primitives from an unlabeled dataset. One of the difficulties of this problem is that in the measurement space, behaviors can be very mixed, despite existing a latent representation where they can be easily separated. We propose a mixture model based on Dirichlet Process (DP) to simultaneously cluster the observed time-series and recover a sparse representation of the behaviors using a Laplacian prior as the base measure of the DP. We show that for linear models, e. g potential functions generated by linear combinations of a large number of features, it is possible to compute analytically the marginal of the observations and derive an efficient sampler. The method is evaluated using robot behaviors and real data from human motion and compared to other techniques.

RLDM Conference 2013 Conference Abstract

Robot learning and control using EEG-based feedback signals

Inaki Iturrate
Jason Omedes
Luis Montesano

In the last years there has been an increasing interest on using human feedback during robot op- eration to incorporate non-expert human expertise while learning complex tasks. Most work has considered reinforcement learning frameworks were human feedback, provided through multiple modalities (speech, graphical interfaces, gestures) is converted into a reward. This paper explores a different communication channel: cognitive EEG brain signals related to the perception of errors by humans. In particular, we con- sider error potentials (ErrP), voltage deflections appearing when a user perceives an error, either committed by herself or by an external machine, thus encoding binary information about how a robot is performing a task. Based on this potential, we propose an algorithm relying on policy matching for inverse reinforcement learning to infer the user goal from brain signals. We present two cases of study involving a target reaching task in a grid world and using a real mobile robot, respectively. For discrete worlds, the results show that the robot is able to infer and reach the target using only error potentials as feedback elicited from human observation. Finally, promising preliminary results were obtained for continuous states and actions in real scenarios.

IROS Conference 2011 Conference Paper

Dense multi-planar scene estimation from a sparse set of images

Alberto Argiles
Javier Civera 0001
Luis Montesano

Ego-motion estimation and 3D scene reconstruction from image data has been a long term aim both in the Robotics and Computer Vision communities. Nevertheless, while both visual SLAM and Structure from Motion already provide an accurate ego-motion estimation, visual scene estimation does not offer yet such a satisfactory result; being in most cases limited to a sparse set of salient points. In this paper we propose an algorithm to densify a sparse point-based reconstruction into a dense multi-plane based one, from the only input of a set of sparse images.

ICRA Conference 2010 Conference Paper

A generalization of the metric-based Iterative Closest Point technique for 3D scan matching

Leopoldo Armesto
Javier Minguez
Luis Montesano

Scan matching techniques have been widely used to compute the displacement of robots. This estimate is part of many algorithms addressing navigation and mapping. This paper addresses the scan matching problem in three dimensional workspaces. We propose an generalization of the Metric based Iterative Closest Point (MbICP) to the 3D case. The main contribution is the development of all the mathematical tools required to formulate the ICP with this new metric, including the derivation of point to plane distances based on the new metric. We also provide experimental results to evaluate the algorithms and different combinations of ICP and MbICP to illustrate the advantages of the metric based approach.

ICRA Conference 2010 Conference Paper

Body schema acquisition through active learning

Ruben Martinez-Cantin
Manuel Lopes 0001
Luis Montesano

We present an active learning algorithm for the problem of body schema learning, i. e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an active learning algorithm to find the optimal set of robot configurations and observations to improve the learning process. By selecting the most informative observations, the proposed method minimizes the required amount of data. We have developed an efficient version of the active learning algorithm to select the points in real-time. The algorithms have been tested and compared using both simulated environments and a real humanoid robot.

ICRA Conference 2010 Conference Paper

Robot reinforcement learning using EEG-based reward signals

Iñaki Iturrate
Luis Montesano
Javier Minguez

Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, there are situations in which this reward is hard to encode, and so would require a supervised approach of reinforcement learning, where a user directly types the reward on each trial. This paper proposes to use brain activity recorded by an EEG-based BCI system as reward signals. The idea is to obtain the reward from the activity generated while observing the robot solving the task. This process does not require an explicit model of the reward signal. Moreover, it is possible to capture subjective aspects which are specific to each user. To achieve this, we designed a new protocol to use brain activity related to the correct or wrong execution of the task. We showed that it is possible to detect and classify different levels of error in single trials. We also showed that it is possible to apply reinforcement learning algorithms to learn new similar tasks using the rewards obtained from brain activity.

ICRA Conference 2009 Conference Paper

Affordance based word-to-meaning association

Verica Krunic
Giampiero Salvi
Alexandre Bernardino
Luis Montesano
José Santos-Victor

This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i. e. , a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. We extend the affordance model to incorporate words. Using verbal descriptions of a task, the model uses temporal co-occurrence to create links between speech utterances and the involved objects, actions and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.

IROS Conference 2007 Conference Paper

Affordance-based imitation learning in robots

Manuel Lopes 0001
Francisco S. Melo
Luis Montesano

In this paper we build an imitation learning algorithm for a humanoid robot on top of a general world model provided by learned object affordances. We consider that the robot has previously learned a task independent affordance-based model of its interaction with the world. This model is used to recognize the demonstration by another agent (a human) and infer the task to be learned. We discuss several important problems that arise in this combined framework, such as the influence of an inaccurate model in the recognition of the demonstration. We illustrate the ideas in the paper with some experimental results obtained with a real robot.

IROS Conference 2007 Conference Paper

Modeling affordances using Bayesian networks

Luis Montesano
Manuel Lopes 0001
Alexandre Bernardino
José Santos-Victor

Affordances represent the behavior of objects in terms of the robot's motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affordance model based on Bayesian networks linking actions, object features and action effects. The network is learnt by the robot through interaction with the surrounding objects. The resulting probabilistic model is able to deal with uncertainty, redundancy and irrelevant information. We evaluate the approach using a real humanoid robot that interacts with objects.

IROS Conference 2006 Conference Paper

Towards the Adaptation of a Robotic Wheelchair for Cognitive Disabled Children

Luis Montesano
Javier Minguez
J. M. Alcubierre
Luis Montano

In this paper, we describe the adaptation of an autonomous robotic wheelchair for cognitive disabled children. The constraints imposed by these users require developing specific human-machine interfaces adapted to their limitations. In most cases it is necessary to develop additional tools to teach the children the spatial relations between the wheelchair, its motion and the environment. In addition to this, it is important to interact closely with the children and their educators. The paper describes the whole process followed to make the children use the autonomous wheelchair and the lessons learnt during the validation phase with the wheelchair and the children

IROS Conference 2005 Conference Paper

Cooperative localization by fusing vision-based bearing measurements and motion

Luis Montesano
José António Gaspar
José Santos-Victor
Luis Montano

This paper presents a method to cooperatively localize pairs of robots fusing bearing-only information provided by cameras and the motion of the vehicles. The algorithm uses the robots as landmarks to estimate their relative location. Bearings are the simplest measurements directly obtained from the cameras, as opposed to measuring depths which would require knowledge or reconstruction of the world structure. We present the general recursive Bayes estimator and three different implementations based on an extended Kalman filter, a particle filter and a combination of both techniques. We have compared the performance of the different implementations using real data acquired with two platforms equipped with omnidirectional cameras and simulated data.

ICRA Conference 2005 Conference Paper

Metric-Based Scan Matching Algorithms for Mobile Robot Displacement Estimation

Javier Minguez
Florent Lamiraux
Luis Montesano

This paper presents a metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The contribution is a geometric distance that takes into account the translation and orientation of the sensor at the same time. This result is used in the two steps of the matching - estimation process. The correspondences between scans are established with this measure and the minimization of the error is also carried out in terms of this distance. As a result, the translation and rotation are compensated in this framework simultaneously. In fact, this is the contribution with respect to previous work that addressed only translation or translation and rotation but separately. The new technique has been implemented and tested on a real vehicle. The experiments illustrate how it is more robust and accurate than prior techniques. At the end of the paper, we give an extension of our distance measure to 3D range-data matching problems.

ICRA Conference 2005 Conference Paper

Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation

Luis Montesano
Javier Minguez
Luis Montano

This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information within a real sensor-based navigation system. The contribution in the modeling aspect is a formulation of the Detection and Tracking of Mobile Objects and the Simultaneous Localization and Map Building in such a way that the nature (static/dynamic) of the observations is included in the estimation process. This is achieved by a set of filters tracking the moving objects and a map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based navigation system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

IROS Conference 2005 Conference Paper

Probabilistic scan matching for motion estimation in unstructured environments

Luis Montesano
Javier Minguez
Luis Montano

This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.

IROS Conference 2004 Conference Paper

An architecture for sensor-based navigation in realistic dynamic and troublesome scenarios

Javier Minguez
Luis Montesano
Luis Montano

We address here a sensor-based navigation system to safely drive vehicles in realistic scenarios. The system is composed of three modules with the following functionalities: model builder, planning and reactive motion. These modules are integrated within a planner-reactor architecture that supervises and coordinates them in order to carry out the motion task. The advantage of our system is to achieve a robust and trustworthy navigation in difficult scenarios, which remain troublesome for many of the existing systems. In order to validate the system, we present experiments with a wheelchair vehicle transporting a human among locations in an office type scenario.

IROS Conference 2004 Conference Paper

Relative localization for pairs of robots based on unidentifiable moving features

Luis Montesano
Luis Montano
Wolfram Burgard

This paper presents a new method for relative localization of a pair of robots based on the trajectories described by unidentifiable moving objects. Our approach uses a Rao-Blackwellized particle filter to estimate both the relative location of the robots and the data associations between the moving objects around the robots. We describe our implementation on real robots and present experiments illustrating the robustness of our algorithm.

IROS Conference 2003 Conference Paper

Identification of moving objects by a team of robots based on kinematic information

Luis Montesano
Luis Montano

This paper describes a method for the identification of moving objects by a team of robots based on kinematic information. The objective is to be able to identify moving objects observed by different robots without using specific landmarks. Our method uses a Bayesian approach and is based on the matching of maps of dynamic objects built by the members of the team. These maps contain the relative position of moving objects and their velocity at a given time. Experimental results using data from a real environment carried out to validate the method are presented and discussed.