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Conference paperEleftheriadis S, Rudovic O, Deisenroth MP, et al., 2016,
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.
, Pages: 154-170 -
Conference paperPesl P, Herrero P, Reddy M, et al., 2016,
Live Demonstration: Smartwatch Implementation of an Advanced Insulin Bolus Calculator for Diabetes
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2370-2370, ISSN: 0271-4302 -
Conference paperMaestre C, Cully AHR, Gonzales C, et al., 2015,
Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach
, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Publisher: IEEEDetermining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.
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Conference paperAthakravi D, Satoh K, Law M, et al., 2015,
Automated inference of rules with exception from past legal cases using ASP
, International Conference on Logic Programming and Non Monotonic Reasoning (LPNMR 2015), Publisher: Springer, Pages: 83-96, ISSN: 0302-9743In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that (i) cover all possible cases (including past cases) within a given set of relevant factors, and (ii) can make deterministic predictions on final verdicts for unseen cases. The extracted declarative model of judgements can then be used to make automated inference of future judgements, and generate explanations of legal decisions.
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Journal articleLaw M, Russo A, Broda K, 2015,
Learning weak constraints in answer set programming
, Theory and Practice of Logic Programming, Vol: 15, Pages: 511-525, ISSN: 1475-3081This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.
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Conference paperKryczka P, Kormushev P, Tsagarakis N, et al., 2015,
Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing
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Conference paperKormushev P, Demiris Y, Caldwell DG, 2015,
Kinematic-free Position Control of a 2-DOF Planar Robot Arm
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Journal articleCarrera A, Palomeras N, Hurtós N, et al., 2015,
Cognitive System for Autonomous Underwater Intervention
, Pattern Recognition Letters, ISSN: 0167-8655 -
Journal articleCully A, Clune J, Tarapore D, et al., 2015,
Robots that can adapt like animals
, Nature, Vol: 521, Pages: 503-507, ISSN: 0028-0836As robots leave the controlled environments of factories to autonomouslyfunction in more complex, natural environments, they will have to respond tothe inevitable fact that they will become damaged. However, while animals canquickly adapt to a wide variety of injuries, current robots cannot "thinkoutside the box" to find a compensatory behavior when damaged: they are limitedto their pre-specified self-sensing abilities, can diagnose only anticipatedfailure modes, and require a pre-programmed contingency plan for every type ofpotential damage, an impracticality for complex robots. Here we introduce anintelligent trial and error algorithm that allows robots to adapt to damage inless than two minutes, without requiring self-diagnosis or pre-specifiedcontingency plans. Before deployment, a robot exploits a novel algorithm tocreate a detailed map of the space of high-performing behaviors: This maprepresents the robot's intuitions about what behaviors it can perform and theirvalue. If the robot is damaged, it uses these intuitions to guide atrial-and-error learning algorithm that conducts intelligent experiments torapidly discover a compensatory behavior that works in spite of the damage.Experiments reveal successful adaptations for a legged robot injured in fivedifferent ways, including damaged, broken, and missing legs, and for a roboticarm with joints broken in 14 different ways. This new technique will enablemore robust, effective, autonomous robots, and suggests principles that animalsmay use to adapt to injury.
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Conference paperJamali N, Kormushev P, Carrera A, et al., 2015,
Underwater Robot-Object Contact Perception using Machine Learning on Force/Torque Sensor Feedback
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Conference paperKormushev P, Demiris Y, Caldwell DG, 2015,
Encoderless Position Control of a Two-Link Robot Manipulator
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Conference paperCarrera A, Palomeras N, Hurtos N, et al., 2015,
Learning multiple strategies to perform a valve turning with underwater currents using an I-AUV
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Conference paperAhmadzadeh SR, Paikan A, Mastrogiovanni F, et al., 2015,
Learning Symbolic Representations of Actions from Human Demonstrations
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Conference paperJamisola RS, Kormushev P, Caldwell DG, et al., 2015,
Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix
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Conference paperLane DM, Maurelli F, Kormushev P, et al., 2015,
PANDORA - Persistent Autonomy through Learning, Adaptation, Observation and Replanning
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Conference paperAthakravi D, Alrajeh D, Broda K, et al., 2015,
Inductive Learning Using Constraint-Driven Bias
, 24th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 16-32, ISSN: 0302-9743- Author Web Link
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- Citations: 5
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Journal articleTakano W, Asfour T, Kormushev P, 2015,
Special Issue on Humanoid Robotics
, Advanced Robotics, Vol: 29 -
Journal articleBimbo J, Kormushev P, Althoefer K, et al., 2015,
Global Estimation of an Object’s Pose Using Tactile Sensing
, Advanced Robotics, Vol: 29 -
Conference paperAhmadzadeh SR, Kormushev P, Caldwell DG, 2014,
Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery
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Journal articleKadir SN, Goodman DFM, Harris KD, 2014,
High-dimensional cluster analysis with the masked EM algorithm
, Neural Computation, Vol: 26, Pages: 2379-2394, ISSN: 0899-7667Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for nextgeneration, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective.We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.
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