Author name cluster

Ruben Martinez-Cantin

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13 papers

2 author rows

ICRA Conference 2023 Conference Paper

Bayesian deep learning for affordance segmentation in images

Lorenzo Mur-Labadia
Ruben Martinez-Cantin
José Jesús Guerrero

Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to learn a probabilistic representation using Monte Carlo dropout. Our results outperform the state-of-the-art of deterministic networks. We attribute this improvement to a better probabilistic feature space representation on the encoder and the Bayesian variability induced at the mask generation, which adapts better to the object contours. We also introduce the new Probability-based Mask Quality measure that reveals the semantic and spatial differences on a probabilistic instance segmentation model. We modify the existing Probabilistic Detection Quality metric by comparing the binary masks rather than the predicted bounding boxes, achieving a finer-grained evaluation of the probabilistic segmentation. We find aleatoric variance in the contours of the objects due to the camera noise, while epistemic variance appears in visual challenging pixels.

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IROS Conference 2023 Conference Paper

Robust Fusion for Bayesian Semantic Mapping

David Morilla-Cabello
Lorenzo Mur-Labadia
Ruben Martinez-Cantin
Eduardo Montijano

The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown data gives too much weight to the outliers and decreases the robustness. To mitigate this issue we propose a novel robust fusion method to combine multiple Bayesian semantic predictions. Our method uses the uncertainty estimation provided by a Bayesian neural network to calibrate the way in which the measurements are fused. This is done by regularizing the observations to mitigate the problem of overconfident outlier predictions and using the epistemic uncertainty to weigh their influence in the fusion, resulting in a different formulation of the probability distributions. We validate our robust fusion strategy by performing experiments on photo-realistic simulated environments and real scenes. In both cases, we use a network trained on different data to expose the model to varying data distributions. The results show that considering the model's uncertainty and regularizing the probability distribution of the observations distribution results in a better semantic segmentation performance and more robustness to outliers, compared with other methods. Video - https://youtu.be/5xVGm7z9c-0

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IJCAI Conference 2019 Conference Paper

Fully Distributed Bayesian Optimization with Stochastic Policies

Javier Garcia-Barcos
Ruben Martinez-Cantin

Bayesian optimization has become a popular method for applications, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. These situations or high-throughput computing, where distributed and scalable architectures are a necessity. However, Bayesian optimization is mostly sequential. Even parallel variants require certain computations between samples, limiting the parallelization bandwidth. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that shows the performance of our method.

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IROS Conference 2018 Conference Paper

Finding safe 3D robot grasps through efficient haptic exploration with unscented Bayesian optimization and collision penalty

João Castanheira
Pedro Vicente
Ruben Martinez-Cantin
Lorenzo Jamone
Alexandre Bernardino

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e. g. , accurate models of known objects or approximate models of familiar objects) or real-time sensing (e. g. , partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e. g. , inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

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ICRA Conference 2017 Conference Paper

Bayesian optimization with adaptive kernels for robot control

Ruben Martinez-Cantin

Active policy search combines the trial-and-error methodology from policy search with Bayesian optimization to actively find the optimal policy. First, policy search is a type of reinforcement learning which has become very popular for robot control, for its ability to deal with complex continuous state and action spaces. Second, Bayesian optimization is a sample efficient global optimization method that uses a surrogate model, like a Gaussian process, and optimal decision making to carefully select each sample during the optimization process. Sample efficiency is of paramount importance when each trial involves the real robot, expensive Monte Carlo runs, or a complex simulator. Black-box Bayesian optimization generally assumes a cost function from a stationary process, because nonstationary modeling is usually based on prior knowledge. However, many control problems are inherently nonstationary due to their failure conditions, terminal states and other abrupt effects. In this paper, we present a kernel function specially designed for Bayesian optimization, that allows nonstationary modeling without prior knowledge, using an adaptive local region. The new kernel results in an improved local search (exploitation), without penalizing the global search (exploration), as shown experimentally in well-known optimization benchmarks and robot control scenarios. We finally show its potential for the design of the wing shape of a UAV.

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IROS Conference 2016 Conference Paper

Unscented Bayesian optimization for safe robot grasping

José Nogueira
Ruben Martinez-Cantin
Alexandre Bernardino
Lorenzo Jamone

Safe and robust grasping of unknown objects is a major challenge in robotics, which has no general solution yet. A promising approach relies on haptic exploration, where active optimization strategies can be employed to reduce the number of exploration trials. One critical problem is that certain optimal grasps discoverd by the optimization procedure may be very sensitive to small deviations of the parameters from their nominal values: we call these unsafe grasps because small errors during motor execution may turn optimal grasps into bad grasps. To reduce the risk of grasp failure, safe grasps should be favoured. Therefore, we propose a new algorithm, unscented Bayesian optimization, that performs efficient optimization while considering uncertainty in the input space, leading to the discovery of safe optima. The results highlight how our method outperforms the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations, finding robust and safe grasps after a few exploration trials.

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JMLR Journal 2014 Journal Article

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

Ruben Martinez-Cantin

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2014. ( edit, beta )

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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.

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IROS Conference 2010 Conference Paper

Gaussian mixture models for affordance learning using Bayesian Networks

Pedro Osório
Alexandre Bernardino
Ruben Martinez-Cantin
José Santos-Victor

Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.

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ICRA Conference 2007 Conference Paper

Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM

Ruben Martinez-Cantin
Nando de Freitas
José A. Castellanos 0001

This paper presents a new particle method, with stochastic parameter estimation, to solve the SLAM problem. The underlying algorithm is rooted on a solid probabilistic foundation and is guaranteed to converge asymptotically, unlike many existing popular approaches. Moreover, it is efficient in storage and computation. The new algorithm carries out filtering only in the marginal filtering space, thereby allowing for the recursive computation of low variance estimates of the map. The paper provides mathematical arguments and empirical evidence to substantiate the fact that the new method represents an improvement over the existing particle filtering approaches for SLAM, which work on the joint path state space.

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IROS Conference 2006 Conference Paper

Adaptive Scale Robust Segmentation for 2D Laser Scanner

Ruben Martinez-Cantin
José A. Castellanos 0001
Juan D. Tardós
J. M. M. Montiel

This paper presents a robust algorithm for segmentation and line detection in 2D range scans. The described method exploits the multimodal probability density function of the residual error. It is capable of segmenting the range data in clusters, estimate the straight segments parameters, and estimate the scale of inliers error noise successfully, despite of high level of spurious data. No prior knowledge about the sensor and object properties is given to the algorithm. The mode seeking is based on mean shift algorithm, which has been widely used and tested in 3D laser scan segmentation, machine learning and pattern recognition applications. We show the reliability of the technique with experimental indoor and outdoor manmade environment. Compared with classical methods, a good compromise between false positive, false negative, wrong segment split and wrong segment merge is achieved, with improved accuracy in the estimated parameters.

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ICRA Conference 2006 Conference Paper

Bounding Uncertainty in EKF-SLAM: the Robocentric Local Approach

Ruben Martinez-Cantin
José A. Castellanos 0001

This paper addresses the consistency issue of the extended Kalman filter approach to the simultaneous localization and mapping (EKF-SLAM) problem. Linearization of the inherent nonlinearities of both the motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency specially in those situations where location uncertainty surpasses a certain threshold. This paper proposes a robocentric local map sequencing algorithm which: (a) bounds location uncertainty within each local map, (b) reduces the computational cost up to constant time in the majority of updates and (c) improves linearization accuracy by updating the map with sensor uncertainty level constraints. Simulation and large-scale outdoor experiments validate the proposed approach

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IROS Conference 2005 Conference Paper

Unscented SLAM for large-scale outdoor environments

Ruben Martinez-Cantin
José A. Castellanos 0001

This paper presents an experimentally validated alternative to the classical extended Kalman filter approach to the solution of the probabilistic state-space simultaneous localization and mapping (SLAM) problem. Several authors have reported the divergence of this classical approach due to the linearization of the inherent nonlinear nature of the SLAM problem. Hence, the approach described in this work aims to avoid the analytical linearization based on Taylor-series expansion of both the model and measurement equations by using the unscented filter. An innovation-based consistency checking validates the feasibility and applicability of the unscented SLAM approach to a real large-scale outdoor exploration mission.

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