Dr Frank Guerin

Senior Lecturer
+44 (0)1483 689195
17 BB 02

Academic and research departments

Department of Computer Science.



Research interests

My teaching

Courses I teach on


My publications


Paulo Abelha, Frank Guerin (2017)Learning how a tool affords by simulating 3D models from the web, In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2017pp. 4923-4929 IEEE
Robots performing everyday tasks such as cooking in a kitchen need to be able to deal with variations in the household tools that may be available. Given a particular task and a set of tools available, the robot needs to be able to assess which would be the best tool for the task, and also where to grasp that tool and how to orient it. This requires an understanding of what is important in a tool for a given task, and how the grasping and orientation relate to performance in the task. A robot can learn this by trying out many examples. This learning can be faster if these trials are done in simulation using tool models acquired from the Web. We provide a semi-automatic pipeline to process 3D models from the Web, allowing us to train from many different tools and their uses in simulation. We represent a tool object and its grasp and orientation using 21 parameters which capture the shapes and sizes of principal parts and the relationships among them. We then learn a `task function' that maps this 21 parameter vector to a value describing how effective it is for a particular task. Our trained system can then process the unsegmented point cloud of a new tool and output a score and a way of using the tool for a particular task. We compare our approach with the closest one in the literature and show that we achieve significantly better results.
FRANK GUERIN (2018)Word Embedding and WordNet Based Metaphor Identification and Interpretation, In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)pp. 1222-1231 Association for Computational Linguistics
Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.
Pawel Gajewski, Paulo Ferreira, Georg Bartels, Chaozheng Wang, Frank Guerin, Bipin Indurkhya, Michael Beetz, Bartlomiej Sniezynski (2019)Adapting Everyday Manipulation Skills to Varied Scenarios, In: 2019 International Conference on Robotics and Automation (ICRA)2019pp. 1345-1351 IEEE
We address the problem of executing tool-using manipulation skills in scenarios where the objects to be used may vary. We assume that point clouds of the tool and target object can be obtained, but no interpretation or further knowledge about these objects is provided. The system must interpret the point clouds and decide how to use the tool to complete a manipulation task with a target object; this means it must adjust motion trajectories appropriately to complete the task. We tackle three everyday manipulations: scraping material from a tool into a container, cutting, and scooping from a container. Our solution encodes these manipulation skills in a generic way, with parameters that can be filled in at run-time via queries to a robot perception module; the perception module abstracts the functional parts of the tool and extracts key parameters that are needed for the task. The approach is evaluated in simulation and with selected examples on a PR2 robot.
FRANK GUERIN (2019)End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories, In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguisticspp. 3888-3898
End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
FRANK GUERIN (2020)Latent Space Factorisation and Manipulation via Matrix Subspace Projection, In: Proceedings of the 37th International Conference on Machine Learning119pp. 5916-5926
We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows us to change selected attributes while preserving other information. Our method, matrix subspace projection, is much simpler than previous approaches to latent space factorisation, for example not requiring multiple discriminators or a careful weighting among their loss functions. Furthermore our new model can be applied to autoencoders as a plugin, and works across diverse domains such as images or text. We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods. The quality of generation of our new model (e.g. reconstruction, conditional generation) is highly competitive to a number of strong baselines.

Additional publications