bibliography.bib

@article{Shafti2016a,
  author = {A. Shafti and R. B. Ribas Manero and A. M. Borg and
                   K. Althoefer and Matthew Howard},
  journal = {IEEE Transactions on Neural Systems and
                   Rehabilitation Engineering},
  title = {Textile-integrated Embroidered Electrodes for
                   Wearable Surface Electromyography},
  year = {2016}
}
@inproceedings{Manero2016,
  author = {R. B. Ribas Manero and B. Michael and A. Shafti and
                   K. Althoefer and J. Ll. Ribas Fernandez and
                   Matthew Howard},
  booktitle = {IEEE International Conference Engineering in Medicine
                   and Biology Society},
  title = {Wearable Embroidered Muscle Activity Sensing Device
                   for the Human Upper Leg},
  year = {2016},
  url = {https://arxiv.org/abs/1602.04841}
}
@inproceedings{Shafti2016,
  author = {A. Shafti and R. B. Ribas Manero and A. M. Borg and
                   K. Althoefer and Matthew Howard},
  booktitle = {IEEE International Conference Robotics and
                   Automation},
  title = {Designing Embroidered Electrodes for Wearable Surface
                   Electromyography},
  year = {2016}
}
@inproceedings{Martinez-Miranda2016,
  author = {Enrique Martinez-Miranda and Peter McBurney and
                   Matthew Howard},
  booktitle = {IEEE International Conference on Evolving and
                   Adaptive Intelligent Systems},
  title = {Learning Unfair Trading: a Market Manipulation
                   Analysis From the Reinforcement Learning Perspective},
  year = {2016}
}
@article{Michael2016a,
  author = {Brendan Michael and Matthew Howard},
  journal = {IEEE Transactions on Neural Systems and
                   Rehabilitation Engineering},
  title = {Learning Predictive Movement Models from
                   Fabric-mounted Wearable Sensors},
  year = {2016}
}
@inproceedings{Lin2015,
  author = {Hsiu-Chin Lin and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Robotics and
                   Automation},
  title = {Learning Null Space Projections},
  year = {2015}
}
@article{Lin2014a,
  author = {Lin,Hsiu-Chin and Matthew Howard and
                   Vijayakumar, Sethu},
  journal = {Robotica},
  month = {12},
  pages = {1225--1244},
  title = {A novel approach for representing and generalising
                   periodic gaits},
  volume = {32},
  year = {2014},
  abstract = {Our goal is to introduce a more appropriate method of
                   representing, generalising and comparing gaits;
                   particularly, walking gait. Human walking gaits are a
                   result of complex, interdependent factors that
                   include variations resulting from embodiments,
                   environment and tasks, making techniques that use
                   average template frameworks suboptimal for systematic
                   analysis or corrective interventions. The proposed
                   work aims to devise methodologies for being able to
                   represent gaits and gait transitions such that
                   optimal policies that eliminate the inter-personal
                   variations from tasks and embodiments may be
                   recovered. Our approach is built upon (i) work in the
                   domain of nullspace policy recovery and (ii) previous
                   work in generalisation for point-to-point movements.
                   The problem is formalised using a walking-phase
                   model, and the nullspace learning method is used to
                   generalise a consistent policy from multiple
                   observations with rich variations. Once recovered,
                   the underlying policies (mapped to different gait
                   phases) can serve as reference guideline to quantify
                   and identify pathological gaits while being robust
                   against interpersonal and task variations. To
                   validate our methods, we have demonstrated robustness
                   of our method with simulated sagittal two-link gait
                   data with multiple ground truth constraints and
                   policies. Pathological gait identification was then
                   tested on real-world human gait data with induced
                   gait abnormality, with the proposed method showing
                   significant robustness to variations in speed and
                   embodiment compared to template-based methods. Future
                   work will extend this to kinetic features and higher
                   dimensional features.},
  doi = {10.1017/S026357471400188X},
  issn = {1469-8668},
  url = {http://journals.cambridge.org/article_S026357471400188X}
}
@inproceedings{Lin2014,
  author = {Hsiu-Chin Lin and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE RAS/EMBS International Conference on Biomedical
                   Robotics and Biomechatronics},
  title = {Generalising Walking Gaits across Subjects and
                   Walking Speeds},
  year = {2014}
}
@inproceedings{Michael2014,
  author = {Brendan Michael and Matthew Howard},
  booktitle = {IEEE International Conference Humanoid Robots},
  title = {Eliminating motion artifacts from fabric-mounted
                   wearable sensors},
  year = {2014}
}
@inproceedings{Sornkarn2014,
  author = {Nantachai Sornkarn and Matthew Howard and
                   Thrishantha Nanayakkara},
  booktitle = {IEEE International Conference Robotics and
                   Automation},
  title = {Internal Impedance Control helps Information Gain in
                   Embodied Perception},
  year = {2014}
}
@inproceedings{Howard2013b,
  author = {Matthew Howard and Yoshihiko Nakamura},
  booktitle = {IEEE International Conference Intelligent Robots and
                   Systems},
  title = {Locally Weighted Least Squares Policy Iteration for
                   Model-free Learning in Uncertain Environments},
  year = {2013}
}
@article{Howard2013a,
  author = {Matthew Howard and David Braun and Sethu Vijayakumar},
  journal = {IEEE Transactions on Robotics},
  number = {4},
  pages = {847-862},
  title = {Transferring Human Impedance Behavior to
                   Heterogeneous Variable Impedance Actuators},
  volume = {29},
  year = {2013},
  abstract = {This paper presents a comparative study of approaches
                   for controlling robots with variable impedance
                   actuators (VIAs), in ways that imitate the behaviour
                   of humans. We focus on problems where impedance
                   modulation strategies are recorded from human
                   demonstrators for transfer to robotic systems with
                   differing levels of heterogeneity, both in terms of
                   the dynamics and actuation. We categorise three
                   classes of approach that may be applied to this
                   problem, namely, (i) direct, (ii) feature-based, and
                   (iii) inverse optimal approaches to transfer. While
                   the first is restricted to highly biomorphic plants,
                   the latter two are shown to be sufficiently general
                   to be applied to various VIAs in a way that is
                   independent of the mechanical design. As
                   instantiations of such transfer schemes, (i) a
                   constraint-based method, and (ii) an apprenticeship
                   learning framework are proposed, and their
                   suitability to different problems in robotic
                   imitation, in terms of efficiency, ease of use and
                   task performance is characterised. The approaches are
                   compared in simulation on systems of varying
                   complexity, and robotic experiments are re- ported
                   for transfer of behaviour from human
                   electromyographic data to two different variable
                   passive compliance robotic devices.},
  doi = {10.1109/TRO.2013.2256311},
  issn = {1552-3098},
  url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?
                  arnumber=6510474}
}
@inproceedings{Howard2013,
  author = {Matthew Howard and Yoshihiko Nakamura},
  booktitle = {European Symposium Artificial Neural Networks},
  title = {Locally Weighted Least Squares Temporal Difference
                   Learning},
  year = {2013}
}
@inproceedings{Radulescu2012,
  author = {Andreea Radulescu and Matthew Howard and David Braun and
                   Sethu Vijayakumar},
  booktitle = {IEEE/ASME International Conference Advanced
                   Intelligent Mechatronics},
  title = {Exploiting Variable Physical Damping in Rapid
                   Movement Tasks},
  year = {2012}
}
@article{Braun2012,
  author = {David Braun and Matthew Howard and Sethu Vijayakumar},
  journal = {Autonomous Robots},
  pages = {237-253},
  title = {Optimal Variable Stiffness Control: Formulation and
                   Application to Explosive Movement Tasks},
  volume = {33},
  year = {2012},
  abstract = {It is widely recognised that compliant actuation is
                   advantageous to robot control once dynamic tasks are
                   considered. However, the benefit of intrinsic
                   compliance comes with high control complexity.
                   Specifically, coordinating the motion of the system
                   through a compliant actuator and finding a
                   task-specific impedance profile that leads to better
                   performance is known to be non-trivial. Here, we
                   propose an optimal control formulation to compute the
                   motor position commands, and the associated
                   time-varying torque and stiffness profiles. To
                   demonstrate the utility of the approach, we consider
                   an "explosive" ball-throwing task where exploitation
                   of the intrinsic dynamics of the compliantly actuated
                   system leads to improved task perfor- mance (i.e.,
                   distance thrown). In this example we show that: (i)
                   the proposed control methodology is able to tailor
                   impedance strategies to specific task objectives and
                   system dynamics, (ii) the ability to vary stiffness
                   can be exploited to achieve better performance, (iii)
                   in systems with variable physical compliance, the
                   present formulation enables exploitation of the
                   energy storage capabilities of the actuators to
                   improve task performance. We illustrate these in
                   numerical simulations, and in hardware experiments on
                   a two-link variable stiffness robot.},
  doi = {10.1007/s10514-012-9302-3},
  url = {http://link.springer.com/article/10.1007%2Fs10514-012-9302-
                  3}
}
@inproceedings{Braun2011,
  address = {Los Angeles, CA, USA},
  author = {David Braun AND Matthew Howard AND Sethu Vijayakumar},
  booktitle = {Robotics: Science and Systems},
  month = jun,
  title = {Exploiting Variable Stiffness in Explosive Movement
                   Tasks},
  year = {2011}
}
@inproceedings{Mori2011,
  author = {Takeshi Mori and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Humanoid Robots},
  title = {Model-free apprenticeship learning for transfer of
                   human impedance behaviour},
  year = {2011}
}
@inproceedings{Howard2011,
  author = {Matthew Howard and David Braun and Sethu Vijayakumar},
  booktitle = {IEEE International Conference Robotics and
                   Automation},
  title = {Constraint-based Equilibrium and Stiffness Control of
                   Variable Stiffness Actuators},
  year = {2011}
}
@inproceedings{Mitrovic2010b,
  author = {Djordje Mitrovic and Stefan Klanke and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Humanoid Robots},
  title = {Exploiting Sensorimotor Stochasticity for Learning
                   Control of Variable Impedance Actuators},
  year = {2010}
}
@inproceedings{Towell2010,
  author = {Chris Towell and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Intelligent Robots and
                   Systems},
  title = {Learning Null Space Policies},
  year = {2010}
}
@inproceedings{Bitzer2010,
  author = {Sebastian Bitzer and Matthew Howard and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Intelligent Robots and
                   Systems},
  title = {Using Dimensionality Reduction to Exploit Constraints
                   in Reinforcement Learning},
  year = {2010}
}
@inproceedings{Howard2010,
  author = {Matthew Howard and Djordje Mitrovic and
                   Sethu Vijayakumar},
  booktitle = {IEEE International Conference Humanoid Robots},
  title = {Transferring Impedance Control Strategies Between
                   Heterogeneous Systems via Apprenticeship Learning},
  year = {2010}
}
@phdthesis{Howard2009d,
  author = {Matthew Howard},
  school = {University of Edinburgh},
  title = {Learning Control Policies from Constrained Motion},
  year = {2009}
}
@inproceedings{Howard2009c,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  booktitle = {IEEE International Conference Intelligent Robots and
                   Systems},
  title = {Robust Constraint-consistent Learning},
  year = {2009},
  doi = {10.1109/IROS.2009.5354663}
}
@article{Howard2009b,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  journal = {Autonomous Robots},
  pages = {105-121},
  title = {A Novel Method for Learning Policies from Variable
                   Constraint Data},
  volume = {27},
  year = {2009},
  abstract = {Many everyday human skills can be framed in terms of
                   performing some task subject to constraints imposed
                   by the environment. Constraints are usually
                   unobservable and frequently change between contexts.
                   In this paper, we present a novel approach for
                   learning (unconstrained) control policies from
                   movement data, where observations come from movements
                   under different constraints. As a key ingredient, we
                   introduce a small but highly effective modification
                   to the standard risk functional, allowing us to make
                   a meaningful comparison between the estimated policy
                   and constrained observations. We demonstrate our
                   approach on systems of varying complexity, including
                   kinematic data from the ASIMO humanoid robot with 27
                   degrees of freedom, and present results for learning
                   from human demonstration.},
  doi = {10.1007/s10514-009-9129-8},
  url = {http://www.springerlink.com/content/r5u85525p6171g17/}
}
@incollection{Howard2009a,
  author = {Matthew Howard and Klanke, S. and Gienger, M. and
                   Goerick, C. and Vijayakumar, S.},
  booktitle = {From Motor to Interaction Learning in Robots},
  publisher = {Springer},
  title = {Methods for Learning Control Policies from
                   Variable-constraint Demonstrations},
  year = {2009}
}
@incollection{Vijayakumar2009,
  author = {Sethu Vijayakumar and Marc Toussaint and
                   Georgios Petkos and Matthew Howard},
  booktitle = {Creating Brain Like Intelligence: From Principles to
                   Complex Intelligent Systems},
  editor = {Sendhoff and Koerner and Sporns and Ritter and Doya},
  publisher = {Springer-Verlag},
  series = {Lecture Notes in Artificial Intelligence},
  title = {Planning and Moving in Dynamic Environments: A
                   statistical machine learning approach},
  volume = {5436},
  year = {2009}
}
@inproceedings{Howard2009,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  booktitle = {IEEE International Conference Robotics and
                   Automation},
  title = {A Novel Method for Learning Policies from Constrained
                   Motion},
  year = {2009}
}
@article{Howard2008a,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  journal = {Applied Bionics and Biomechanics},
  month = dec,
  number = {4},
  pages = {195-211},
  title = {Behaviour Generation in Humanoids by Learning
                   Potential-based Policies from Constrained Motion},
  volume = {5},
  year = {2008},
  doi = {10.1080/11762320902789830},
  url = {http://www.tandfonline.com/doi/full/10.1080/
                  11762320902789830}
}
@inproceedings{Howard2008,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  booktitle = {IEEE International Conference Humanoid Robots},
  title = {Learning Potential-based Policies from Constrained
                   Motion},
  year = {2008}
}
@inproceedings{Howard2007,
  author = {Matthew Howard and Sethu Vijayakumar},
  booktitle = {Workshop on Robotics and Mathematics},
  month = {September},
  title = {Reconstructing Null-space Policies Subject to Dynamic
                   Task Constraints in Redundant Manipulators},
  year = {2007}
}
@inproceedings{Howard2006,
  author = {Matthew Howard and Michael Gienger and
                   Christian Goerick and Sethu Vijayakumar},
  booktitle = {IEEE International Conference on Robotics and
                   Biomimetics},
  title = {Learning Utility Surfaces for Movement Selection},
  year = {2006}
}