Papers by Veronica Arriola-Rios
In this paper, we present a multimodal framework for offline learning of generative models of obj... more In this paper, we present a multimodal framework for offline learning of generative models of object deformation under robotic pushing. The model is multimodal in that it is based on integrating force and visual information. The framework consists of several submodels that are independently calibrated from the same data. These component models can be sequenced to provide many-step prediction and classification. When presented with a test example—a robot finger pushing a deformable object made of an unidentified, but previously learned, material—the predictions of modules for different materials are compared so as to classify the unknown material. Our approach, which consists of offline learning and combination of multiple models, goes beyond previous techniques by enabling: 1) predictions over many steps; 2) learning of plastic and elastic deformation from real data; 3) prediction of forces experienced by the robot; 4) classification of materials from both force and visual data; and...
Every day environments contain a great variety of deformable objects and it is not possible to pr... more Every day environments contain a great variety of deformable objects and it is not possible to program a robot in advance to know about their characteristic behaviours. For this reason, robots have been highly successful in manoeuvring deformable objects mainly in the industrial sector, where the types of interactions are predictable and highly restricted, but research in everyday environments remains largely unexplored. The contributions of this thesis are: i) the application of an elastic/plastic mass-spring method to model and predict the behaviour of deformable objects manipulated by a robot; ii) the automatic calibration of the parameters of the model, using images of real objects as ground truth; iii) the use of piece-wise regression curves to predict the reaction forces, and iv) the use of the output of this force prediction model as input for the mass-spring model which in turn predicts object deformations; v) the use of the obtained models to solve a material classification...
Frontiers in Robotics and AI
Manipulation of deformable objects has given rise to an important set of open problems in the fie... more Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.
cs.bham.ac.uk
Artificial Intelligence (AI) and Animal Cognition(AC) share a common goal: to study learning and ... more Artificial Intelligence (AI) and Animal Cognition(AC) share a common goal: to study learning and causal understanding. However, the perspectives are completely different: while AC studies intelligent systems present in nature, AI tries to to build them almost from scratch. It is proposed here that both visions are complementary and should interact more to better achieve their ends. Nonetheless, before efficient collaboration can take place, a greater mutual understanding ofeach field is required, beginning with clarifications of their respective terminologies and considering the constraints of the research in each field.
Artificial Intelligence (AI) and Animal Cognition (AC) share a common goal: to study learning and... more Artificial Intelligence (AI) and Animal Cognition (AC) share a common goal: to study learning and causal understanding. However, the perspectives are completely different: while AC studies intelligent systems present in nature, AI tries to to build them almost from scratch. It is proposed here that both visions are complementary and should interact more to better achieve their ends. Nonetheless, before efficient collaboration can take place, a greater mutual understanding of each field is required, beginning with clarifications of their respective terminologies and considering the constraints of the research in each field. 12
Research and Development in Intelligent Systems XXVIII, 2011
Deformable objects abound in nature, and future robots must be able to predict how they are going... more Deformable objects abound in nature, and future robots must be able to predict how they are going to behave in order to control them. In this paper we present a method capable of learning to predict the behaviour of deformable objects. We use a mass-spring-like model, which we extended to better suit our purposes, and apply it to the concrete scenario of robotic manipulation of an elastic deformable object. We describe a procedure for automatically calibrating the parameters for the model taking images and forces from a real sponge as ground truth. We use this ground truth to provide error measures that drive an evolutionary process that searches the parameter space of the model. The resulting calibrated model can make good predictions for 200 frames (6.667 seconds of real time video) even when tested with forces being applied in different positions to those trained.
Behavioural Processes, 2012
Imagine a situation in which you had to design a physical agent that could collect information fr... more Imagine a situation in which you had to design a physical agent that could collect information from its environment, then store and process that information to help it respond appropriately to novel situations. What kinds of information should it attend to? How should the information be represented so as to allow efficient use and re-use? What kinds of constraints and trade-offs would there be? There are no unique answers. In this paper, we discuss some of the ways in which the need to be able to address problems of varying kinds and complexity can be met by different information processing systems. We also discuss different ways in which relevant information can be obtained, and how different kinds of information can be processed and used, by both biological organisms and artificial agents. We analyse several constraints and design features, and show how they relate both to biological organisms, and to lessons that can be learned from building artificial systems. Our standpoint overlaps with in that we assume that a collection of mechanisms geared to learning and developing in biological environments are available in forms that constrain, but do not determine, what can or will be learnt by individuals. (J. Chappell). requirements and designs of different biological information processing systems. It intersects with existing work on motivation, play, learning, perception and development, and should help us understand how such systems have evolved and how they develop within an individual's lifetime.
Abstract. Artificial Intelligence (AI) and Animal Cognition (AC) share a common goal: to study le... more Abstract. Artificial Intelligence (AI) and Animal Cognition (AC) share a common goal: to study learning and causal understanding. However, the perspectives are completely different: while AC studies intelligent systems present in nature, AI tries to to build them almost from scratch. It is proposed here that both visions are complementary and should interact more to better achieve their ends. Nonetheless, before efficient collaboration can take place, a greater mutual understanding of each field is required, beginning with clarifications of their ...
Studies in Applied Philosophy, Epistemology and Rational Ethics, 2013
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Papers by Veronica Arriola-Rios