Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of demonstrations, such that they can be used to optimise an autonomous controller to avoid singularity, without having to explicitly know the task constraints. The proposed approach avoids singularity, and thereby unpredictable behaviour when carrying out a task, by maximising the learnt manipulability throughout the motion of the constrained system, and is not limited to kinematic systems. Its benefits are demonstrated through comparisons with other control policies which show that the constrained manipulability of a system learnt through demonstration can be used to avoid singularities in cases where these other policies would fail. In the absence of the systems manipulability subject to a tasks constraints, the proposed approach can be used instead to infer these with results showing errors less than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world robotic system

RedFeather- resource exhibition and discovery: a lightweight micro-repository for resource sharing

Open Educational Resources (OERs) depend on being hosted in repositories so that they can be effectively viewed, managed, searched and indexed online. Content – especially multimedia content – that is not hosted in this way has no metadata and is effectively dark to the wider community. Similarly content that is not described properly, and with appropriate licenses, is of limited use. This is a challenge for small-scale contributors, such as individuals and small projects, as the overhead of setting up and administrating a content repository can be prohibitive.In this paper we propose RedFeather, a micro-repository, as a solution to this problem. RedFeather is a simple and straightforward server-side tool that requires zero to little configuration, but that provides the core functionality of a fully-fledged OER repository, including: resource pages with inline preview, a resource manager with streamlined workflow, and views of the resource in OER critical formats (including RDF, JSON, and RSS). RedFeather is fully customizable, with a flexible plugin architecture and configurable templates, but also works without any customization as a single php script file uploaded to a web server. The goal of a micro-repository like RedFeather is both to enable small-scale contributors to easily join the OER community, and to act as a intermediate step for larger contributors beginning a collection, or requiring a temporary home for their resources while a more substantial repository is developed. Our hope is that by lowering the barriers to participation, RedFeather can help the OER community to take advantage of the long tail of small to medium sized content creators

Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation

Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions, as it is not feasible for a person to provide a demonstration set that accounts for all possible variations in non-trivial tasks. While there are many learning methods that can handle interpolation of observed data effectively, extrapolation from observed data offers a much greater challenge. To address this problem of generalisation, this paper proposes a modified Task-Parameterised Gaussian Mixture Regression method that considers the relevance of task parameters during trajectory generation, as determined by variance in the data. The benefits of the proposed method are first explored using a simulated reaching task data set. Here it is shown that the proposed method offers far-reaching, low-error extrapolation abilities that are different in nature to existing learning methods. Data collected from novice users for a real-world manipulation task is then considered, where it is shown that the proposed method is able to effectively reduce grasping performance errors by ${\sim30\%}$ and extrapolate to unseen grasp targets under real-world conditions. These results indicate the proposed method serves to benefit novice users by placing less reliance on the user to provide high quality demonstration data sets.Comment: 8 pages, 6 figures, submitted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

COIN is dead - long live transformation

Donald Rumsfeld was right. Force transformation works. The techniques that led to the initial victories in Afghanistan in 2001 were precisely those that produced success in Libya in 2011.1 Small-scale deployments of special forces backed by precision strike and deep attack capabilities used to support an allied indigenous armed group proved an effective military tool for achieving specific strategic outcomes. In contrast, the results of large-scale troop deploy- ments as part of counterinsurgency (COIN), stabilization and nation-building activities over the past ten years in Iraq and Afghanistan have been less defini- tive. Despite intensive investment in blood, treasure, and military effort, the precise long-term outcomes of these two campaigns remain unclear and will be open to debate for years to come. This challenging operational experience has, however, highlighted some necessary and enduring truths about the use of military force. This paper explores those in light of the last ten years of counterinsurgenc

Some Nearly Quantum Theories

We consider possible non-signaling composites of probabilistic models based on euclidean Jordan algebras. Subject to some reasonable constraints, we show that no such composite exists having the exceptional Jordan algebra as a direct summand. We then construct several dagger compact categories of such Jordan-algebraic models. One of these neatly unifies real, complex and quaternionic mixed-state quantum mechanics, with the exception of the quaternionic "bit". Another is similar, except in that (i) it excludes the quaternionic bit, and (ii) the composite of two complex quantum systems comes with an extra classical bit. In both of these categories, states are morphisms from systems to the tensor unit, which helps give the categorical structure a clear operational interpretation. A no-go result shows that the first of these categories, at least, cannot be extended to include spin factors other than the (real, complex, and quaternionic) quantum bits, while preserving the representation of states as morphisms. The same is true for attempts to extend the second category to even-dimensional spin-factors. Interesting phenomena exhibited by some composites in these categories include failure of local tomography, supermultiplicativity of the maximal number of mutually distinguishable states, and mixed states whose marginals are pure.Comment: In Proceedings QPL 2015, arXiv:1511.0118

Composites and Categories of Euclidean Jordan Algebras

We consider possible non-signaling composites of probabilistic models based on euclidean Jordan algebras (EJAs), satisfying some reasonable additional constraints motivated by the desire to construct dagger-compact categories of such models. We show that no such composite has the exceptional Jordan algebra as a direct summand, nor does any such composite exist if one factor has an exceptional summand, unless the other factor is a direct sum of one-dimensional Jordan algebras (representing essentially a classical system). Moreover, we show that any composite of simple, non-exceptional EJAs is a direct summand of their universal tensor product, sharply limiting the possibilities. These results warrant our focussing on concrete Jordan algebras of hermitian matrices, i.e., euclidean Jordan algebras with a preferred embedding in a complex matrix algebra}. We show that these can be organized in a natural way as a symmetric monoidal category, albeit one that is not compact closed. We then construct a related category InvQM of embedded euclidean Jordan algebras, having fewer objects but more morphisms, that is not only compact closed but dagger-compact. This category unifies finite-dimensional real, complex and quaternionic mixed-state quantum mechanics, except that the composite of two complex quantum systems comes with an extra classical bit. Our notion of composite requires neither tomographic locality, nor preservation of purity under tensor product. The categories we construct include examples in which both of these conditions fail. In such cases, the information capacity (the maximum number of mutually distinguishable states) of a composite is greater than the product of the capacities of its constituents.Comment: 60 pages, 3 tables. Substantially revised, with some new result