1,958 research outputs found
An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems
Non-linear dynamical systems represent a compact, flexible, and robust tool
for reactive motion generation. The effectiveness of dynamical systems relies
on their ability to accurately represent stable motions. Several approaches
have been proposed to learn stable and accurate motions from demonstration.
Some approaches work by separating accuracy and stability into two learning
problems, which increases the number of open parameters and the overall
training time. Alternative solutions exploit single-step learning but restrict
the applicability to one regression technique. This paper presents a
single-step approach to learn stable and accurate motions that work with any
regression technique. The approach makes energy considerations on the learned
dynamics to stabilize the system at run-time while introducing small deviations
from the demonstrated motion. Since the initial value of the energy injected
into the system affects the reproduction accuracy, it is estimated from
training data using an efficient procedure. Experiments on a real robot and a
comparison on a public benchmark shows the effectiveness of the proposed
approach.Comment: Accepted at the International Conference on Robotics and Automation
202
SiteWit Corporation: SQL or NoSQL that is the Question
This teaching case focuses on a start-up company in the Web analytics and online advertising space, which faces a database scaling challenge. The case covers the rapidly emerging NoSQL database products that can be used to implement very large distributed databases. These are exciting times in the database marketplace, with a flock of new companies offering scalable database systems for the cloud. These products challenge the existing relational database vendors that have come to dominate the market. The case outlines four potential solutions and asks students to make a choice or suggest a different alternative
Stable Motion Primitives via Imitation and Contrastive Learning
Learning from humans allows non-experts to program robots with ease, lowering
the resources required to build complex robotic solutions. Nevertheless, such
data-driven approaches often lack the ability to provide guarantees regarding
their learned behaviors, which is critical for avoiding failures and/or
accidents. In this work, we focus on reaching/point-to-point motions, where
robots must always reach their goal, independently of their initial state. This
can be achieved by modeling motions as dynamical systems and ensuring that they
are globally asymptotically stable. Hence, we introduce a novel Contrastive
Learning loss for training Deep Neural Networks (DNN) that, when used together
with an Imitation Learning loss, enforces the aforementioned stability in the
learned motions. Differently from previous work, our method does not restrict
the structure of its function approximator, enabling its use with arbitrary
DNNs and allowing it to learn complex motions with high accuracy. We validate
it using datasets and a real robot. In the former case, motions are 2 and 4
dimensional, modeled as first- and second-order dynamical systems. In the
latter, motions are 3, 4, and 6 dimensional, of first and second order, and are
used to control a 7DoF robot manipulator in its end effector space and joint
space. More details regarding the real-world experiments are presented in:
\url{https://youtu.be/OM-2edHBRfc}
Learning Riemannian Stable Dynamical Systems via Diffeomorphisms
Dexterous and autonomous robots should be capable of executing elaborated
dynamical motions skillfully. Learning techniques may be leveraged to build
models of such dynamic skills. To accomplish this, the learning model needs to
encode a stable vector field that resembles the desired motion dynamics. This
is challenging as the robot state does not evolve on a Euclidean space, and
therefore the stability guarantees and vector field encoding need to account
for the geometry arising from, for example, the orientation representation. To
tackle this problem, we propose learning Riemannian stable dynamical systems
(RSDS) from demonstrations, allowing us to account for different geometric
constraints resulting from the dynamical system state representation. Our
approach provides Lyapunov-stability guarantees on Riemannian manifolds that
are enforced on the desired motion dynamics via diffeomorphisms built on neural
manifold ODEs. We show that our Riemannian approach makes it possible to learn
stable dynamical systems displaying complicated vector fields on both
illustrative examples and real-world manipulation tasks, where Euclidean
approximations fail.Comment: To appear at CoRL 202
A Fast Sensitivity-Based Preventive Control Selection Method for Online Voltage Stability Assessment
Deep Metric Imitation Learning for Stable Motion Primitives
Imitation Learning (IL) is a powerful technique for intuitive robotic
programming. However, ensuring the reliability of learned behaviors remains a
challenge. In the context of reaching motions, a robot should consistently
reach its goal, regardless of its initial conditions. To meet this requirement,
IL methods often employ specialized function approximators that guarantee this
property by construction. Although effective, these approaches come with a set
of limitations: 1) they are unable to fully exploit the capabilities of modern
Deep Neural Network (DNN) architectures, 2) some are restricted in the family
of motions they can model, resulting in suboptimal IL capabilities, and 3) they
require explicit extensions to account for the geometry of motions that
consider orientations. To address these challenges, we introduce a novel
stability loss function, drawing inspiration from the triplet loss used in the
deep metric learning literature. This loss does not constrain the DNN's
architecture and enables learning policies that yield accurate results.
Furthermore, it is easily adaptable to the geometry of the robot's state space.
We provide a proof of the stability properties induced by this loss and
empirically validate our method in various settings. These settings include
Euclidean and non-Euclidean state spaces, as well as first-order and
second-order motions, both in simulation and with real robots. More details
about the experimental results can be found at: https://youtu.be/ZWKLGntCI6w.Comment: 21 pages, 15 figures, 4 table
East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series
Academic geriatric medicine in Leicester
.
There has never been a better time to consider joining us. We have recently appointed a
Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton,
who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic
opportunities to support students in their academic pursuits through a well-established
intercalated BSc programme, and routes on through such as ACF posts, and a successful
track-record in delivering higher degrees leading to ACL post. We collaborate strongly
with Health Sciences, including academic primary care. See below for more detail on our
existing academic set-up.
Leicester Academy for the Study of Ageing
We are also collaborating on a grander scale, through a joint academic venture focusing
on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the
local health service providers (acute and community), De Montfort University; University
of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK.
Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been
joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen
Harrison Dening has also recently been appointed an Honorary Chair.
LASA aims to improve outcomes for older people and those that care for them that takes
a person-centred, whole system perspective. Our research will take a global perspective,
but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland,
including building capacity. We are undertaking applied, translational, interdisciplinary
research, focused on older people, which will deliver research outcomes that address
domains from: physical/medical; functional ability, cognitive/psychological; social or
environmental factors. LASA also seeks to support commissioners and providers alike for
advice on how to improve care for older people, whether by research, education or
service delivery. Examples of recent research projects include: ‘Local History Café’
project specifically undertaking an evaluation on loneliness and social isolation; ‘Better
Visits’ project focused on improving visiting for family members of people with dementia
resident in care homes; and a study on health issues for older LGBT people in Leicester.
Clinical Geriatric Medicine in Leicester
We have developed a service which recognises the complexity of managing frail older
people at the interface (acute care, emergency care and links with community services).
There are presently 17 consultant geriatricians supported by existing multidisciplinary
teams, including the largest complement of Advance Nurse Practitioners in the country.
Together we deliver Comprehensive Geriatric Assessment to frail older people with
urgent care needs in acute and community settings.
The acute and emergency frailty units – Leicester Royal Infirmary
This development aims at delivering Comprehensive Geriatric Assessment to frail older
people in the acute setting. Patients are screened for frailty in the Emergency
Department and then undergo a multidisciplinary assessment including a consultant
geriatrician, before being triaged to the most appropriate setting. This might include
admission to in-patient care in the acute or community setting, intermediate care
(residential or home based), or occasionally other specialist care (e.g. cardiorespiratory).
Our new emergency department is the county’s first frail friendly build and includes
fantastic facilities aimed at promoting early recovering and reducing the risk of hospital
associated harms.
There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we
have been examining geriatric outreach to oncology and surgery as part of an NIHR
funded study.
We are home to the Acute Frailty Network, and those interested in service developments
at the national scale would be welcome to get involved.
Orthogeriatrics
There are now dedicated hip fracture wards and joint care with anaesthetists,
orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone
disease that run clinics.
Community work
Community work will consist of reviewing patients in clinic who have been triaged to
return to the community setting following an acute assessment described above.
Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will
work closely with local GPs with support from consultants to deliver post-acute, subacute,
intermediate and rehabilitation care services.
Stroke Medicine
24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK
and along with the high standard of vascular surgery locally means one of the best
performances regarding carotid intervention
- …