39 research outputs found
A Personal Robot as an Improvement to the Customers’ In- Store Experience
Robotics is a growing industry with applications in numerous markets, including retail, transportation, manufacturing, and even as personal assistants. Consumers have evolved to expect more from the buying experience, and retailers are looking at technology to keep consumers engaged. In today’s highly competitive business climate, being able to attract, serve, and satisfy more customers is a key to success. It is our belief that smart robots will play a significant role in physical retail in the future. One successful example is wGO, a robotic shopping assistant developed by Follow Inspiration. The wGO is an autonomous and self-driven shopping cart, designed to follow people with reduced mobility (the elderly, people in wheelchair, pregnant women, those with temporary reduced mobility, etc.) in commercial environments. With the Retail Robot, the user can control the shopping cart without the need to push it. This brings numerous advantages and a higher level of comfort since the user does not need to worry about carrying the groceries or pushing the shopping cart. The wGO operates under a vision-guided approach based on user-following with no need for any external device. Its integrated architecture of control, navigation, perception, planning, and awareness is designed to enable the robot to successfully perform personal assistance, while the user is shopping
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Modeling human expertise for providing adaptive levels of robot shared autonomy
In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Processes (POMDPs) to represent a model of the user's expertise in controlling the robot. The POMDP uses observations from the user's actions and from the environment to update the belief of the user's skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. The macro-action controllers encompass varying levels of robot autonomy and reduce the space of the POMDP, removing the need to plan over separate actions. As part of this research, we ran two users study, developed a method to automatically generate macro-action controller values, and applied our user expertise model to provide shared autonomy on a semi-autonomous underwater vehicle. In our first user study, we tested our user expertise model in a robot driving simulation. Users drove a simulated robot through an obstacle-filled map while the POMDP model chose appropriate macro-action controllers based on the belief state of the user's skill level. The results of the user study showed that our model can encapsulate user skill levels. The results also showed that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill. We designed a controller value synthesis method to generate the variables that control the levels of autonomy in the macro-action controllers. We found differences in how the users drive the robot using a decision tree generated from the data recorded in the first user study, and we used these differences to program simulated user ``bots'' that mimic users of different skill levels. The ``bots'' were used to test a range of variables for the controllers, and the controller variables were found from minimizing obstacles hit, time to complete maps, and total distance driven from the simulated data.For our second user study, we looked at users' satisfaction without robot autonomy, with the highest amount of autonomy, and with the autonomy chosen by our expertise model. We found users we classified as beginners ranked the autonomy more favorably than those ranked as experts. We implemented our expertise model on a Seabotix vLBV300 underwater vehicle and ran a trial off the coast of Newport, Oregon. During our trials, we recorded a user driving the vehicle to predetermined waypoints. When beginner actions were performed, the user expertise model provided an increased level of autonomy which either increased throttle when far from waypoints or decreased throttle when close to waypoints. This demonstrated an implementation of our algorithm on existing robot hardware in the field.Keywords: Shared autonomy, Robotics, Human-robot interactio
Development of EEG-based technologies for the characterization and treatment of neurological diseases affecting the motor function
This thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present i) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, ii) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally iii) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions. The presented thesis has yielded advances from both the technological and the scientific points of view in all studies proposed. The main contributions from the technological point of view are: ¿ The design of an integrated upper-limb platform working in real-time. The platform was designed to acquire information from different types of noninvasive sensors (EEG, EMG and gyroscopic sensors) characterizing the planning and execution of voluntary movements. The platform was also capable of processing online the acquired data and generating an electrical feedback. ¿ The development of signal processing and classifying techniques adapted to the kind of signal recorded in the two kinds of patients considered in this thesis (patients with essential tremor and patients with a stroke) and to the requirements of online processing and real-time single-trial function desired for BCI applications. Especially in this regard, an original methodology to detect onsets of voluntary movements using slow cortical potentials and cortical rhythms has been presented. ¿ The design and validation in real-time of asynchronous BCI systems using motor planning EEG segments to anticipate or detect when patients begin a voluntary movement with the upper-limb. ¿ The proof of concept of the advantages of an EEG system integrated in a multimodal human-robot interface architecture that constitutes the first multimodal interface using the combined acquisition of EEG, EMG and gyroscopic data, which allows the concurrent characterization of different parts of the body associated with the execution of a movement. The main scientific contributions of this thesis are: ¿ The study of the EEG-based anticipation of voluntary movements presented in Chapter 5 of the thesis was the first demonstration (to the author's knowledge) of the capacity of the EEG signal to provide reliable movement predictions based on single-trial classification of online data of healthy subjects and ET patients. This study also provides, for the first time, the results of a BCI system tested in ET patients and it represents an original approach to BCI applications for this group of patients. ¿ It has been presented the first neurophysiological study using EEG and EMG data to analyze the effects of a drug on cortical activity and tremors of patients with ET. In addition, the obtained results have shown for the first time that a significant correlation exists between the dynamics of specific cortical oscillations and pathological tremor manifestation as a consequence of the drug effects. ¿ It has been proposed for the first time an experiment to inspect whether the EEG signal carries enough information to classify up to seven different tasks performed with a single limb. Both the methodology applied and the validation procedure are also innovative in this sort of studies. ¿ It has been demonstrated for the first time the relevance of combining different cortical sources of information (such as BP and ERD) to estimate the initiation of voluntary movements with the upper-limb. In this line, special relevance may be given to the positive results achieved with stroke patients, improving the results presented by similar previous EEG-based studies by other research groups. It has also been proposed for the first time an upper-limb intervention protocol for stroke patients using BP and ERD patterns to provide proprioceptive feedback tightly associated with the patients' expectations of movement. The effects of the proposed intervention have been studied with a small group of patients
Resilience against misbehaving nodes in asynchronous networks
When dealing with network systems, a fundamental challenge is to ensure their functioning even when some of the network nodes do not operate as intended due to faults or attacks. The objective of this paper is to address the problem of resilient consensus in a context where the nodes have their own clocks, possibly operating in an asynchronous way, and can make updates at arbitrary time instants. The results represent a first step towards the development of resilient event-triggered and self-triggered coordination protocols. (C) 2019 Elsevier Ltd. All rights reserved