Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion

Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive, requiring large training datasets and replay buffers to achieve performant results. This poses a challenge for the next generation of field robots that will need to learn on the edge to adapt to their environment. In this paper, we begin to address this issue through observation space quantization. We evaluate our approach using four simulated robot locomotion tasks and two state-of-the-art DRL algorithms, the on-policy Proximal Policy Optimization (PPO) and off-policy Soft Actor-Critic (SAC) and find that observation space quantization reduces overall memory costs by as much as 4.2x without impacting learning performance.Comment: Accepted to ICRA 202

Análise comparativa dos Sistemas de Saúde do Haiti e do Brasil enquanto ao Modelo de Gestão, Modelo de Atenção, Modelo de Financiamento e Recursos Humanos

Trabalho de conclusão de curso de graduação do curso de SAÚDE COLETIVA apresentado ao Instituto Latinoamericano de Ciências da Vida e Natureza (ILACVN) da Universidade Federal da Integração Latinoamericana (UNILA) como parcial para obter o título de Bacharel em Saúde Coletiva. Orientador: Prof. Dr. Dr. Antulio Hoyos RiveraO presente trabalho visa analisar comparativamente alguns aspectos relevantes dos sistemas de saúde do Haiti e do Brasil, de forma a buscar elementos específicos à gestão das políticas públicas no setor da saúde. Ressaltando que o Brasil representa a maior economia da America Latina, enquanto o Haiti a pior economia do continente Americano. A partir de dados obtidos em bases de informação com acesso público sobre tais sistemas de saúde e revisão de literatura acadêmica em caracterização de sistemas de saúde internacionais, efetuou-se uma síntese dos seguintes elementos, tais como o modelo de gestão, modelo de atenção, modelo de financiamento e dos recursos humanos. A partir desses aspectos identificamos as semelhanças e as diferenças entre sistemas de saúde do Brasil e do Haiti.The present study aims to comparatively analyze some relevant aspects of the health systems of Haiti and Brazil, in order to seek specific elements to the management of public policies in the health sector. Noting that Brazil represents the largest economy in Latin America, while Haiti is the worst economy in the Americas. Based on data obtained from information bases with public access to such health systems and review of the academic literature on the characterization of international health systems, a synthesis of the following elements was made, such as the management model, care model, model of funding and human resources. From these aspects we identify the similarities and differences between health systems in Brazil and Hait

Manifestações culturais da América Latina por uma saúde humanizada IV

Projeto de extensão desenvolvido entre o primeiro semestre de 2017 e o segundo de 2018 por docentes e discentes da Universidade Federal da Integração Latino Americana (UNILA) em parceria com o Centro de Atenção Psicossocial Álcool e Drogas (Capsad) de Foz do Iguaçu – PR. Organizamos, sob a supervisão da Direção do Centro, uma série de oficinas destinadas aos frequentadores, com periodicidade quinzenal, sobre temática latino-americana e relacionadas com a Terapia Ocupacional (TO). Como resultados da ação foram produzidas 84 caixas MDF e 42 peças de cerâmica pintadas, cuja elaboração, exposição e/ou posterior presenteado entre familiares, amigos e visitantes do Centro contribuiu para o fortalecimento da sinergia dos usuários, a sua auto estima e abriu o caminho para futuras colaborações entre o Centro e a Universidade. Ao mesmo tempo, fizemos pesquisa sobre a organização e o funcionamento do Capsad de Foz do Iguaçu, bem como do perfil dos usuários frequentadores. Concluímos que a maioria pertence ao gênero masculino, sua idade oscila entre os 23 e 46 anos, possui uma formação acadêmica que não atinge o ensino fundamental completo, vive em situação de desemprego, sua experiência laboral anterior estava relacionada com trabalhos braçais e está em situação econômica desfavorável

TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC both by benchmarking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 g quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance.Comment: First three authors contributed equally and are ordered alphabeticall

Sustained Acoustic Medicine Combined with A Diclofenac Ultrasound Coupling Patch for the Rapid Symptomatic Relief of Knee Osteoarthritis: Multi-Site Clinical Efficacy Study

Background: Sustained Acoustic Medicine (SAM) is an emerging, non-invasive, non-narcotic, home-use ultrasound therapy for the daily treatment of joint pain. The aim of this multi-site clinical study was to examine the efficacy of long-duration continuous ultrasound combined with a 1% diclofenac ultrasound gel patch in treating pain and improving function in patients with knee osteoarthritis. Methods: The Consolidated Standards of Reporting Trials (CONSORT) were followed. Thirty-two (32) patients (18-males, 14-females) 54 years of average age with moderate to severe knee pain and radiographically confirmed knee osteoarthritis (Kellgren-Lawrence (KL) grade II/III) were enrolled for treatment with the SAM device and diclofenac patch applied daily to the treated knee. SAM ultrasound (3 MHz, 0.132 W/cm2, 1.3 W) and 6 grams of 1% diclofenac were applied with a wearable device for 4 hours daily for 1 week, delivering 18,720 Joules of ultrasound energy per treatment. The primary outcome was the daily change in pain intensity using a numeric rating scale (NRS 0-10), which was assessed prior to intervention (baseline, day 1), before and after each daily treatment, and after 1 week of daily treatment (day 7). Rapid responders were classified as those patients exhibiting greater than a 1-point reduction in pain following the first treatment. Change in Western Ontario McMaster Osteoarthritis Questionnaire (WOMAC) score from baseline to day 7 was the secondary functional outcome measure. Additionally, a series of daily usability and user experience questions related to devising ease of use, functionality, safety, and effectiveness, were collected. Data were analyzed using t-tests and repeated measure ANOVAs. Results: The study had a 94% retention rate, and there were no adverse events or study-related complaints across 224 unique treatment sessions. Rapid responders included 75% of the study population. Patients exhibited a significant mean NRS pain reduction over the 7-day study of 2.06-points (50%) for all subjects (n=32, p Conclusion: Sustained Acoustic Medicine combined with 1% topical diclofenac rapidly reduced pain and improved function in patients with moderate to severe osteoarthritis-related knee pain. The clinical findings suggest that this treatment approach may be used as a conservative, non-invasive treatment option for patients with knee osteoarthritis. Additional research is warranted on non-weight bearing joints of the musculoskeletal system as well as different topical drugs that could benefit from improved localized delivery

A virtual reality paradigm to assess episodic memory: Validation-dataset for six parallel versions and a structured behavioral assessment

In the epilepsy monitoring unit of the Department of Neurology at the University Clinic of Salzburg 20 adult patients were recruited to participate in a validation of 6 parallel versions of the virtual reality test for episodic memory. Patients were tested up to 7 times, i.e. twice a day, in the morning and evening, beginning on Monday evening. Each session consisted of learning a new town and immediate recall for this town. All sessions but the first one included also delayed recall of the previously learned town and a recognition test. Recall included the sub-scales what, details, when, egocentric where and allocentric where. Recognition memory was tested by presenting the patients 30 sentences of which 15 were true and 15 were false. While not all patients completed the full testing schedule, at immediate recall for 9 patients a full data set (7 sessions) is available. All patients were free of antiepileptic medication (N = 19) or medication was kept constant across the week (N = 1). This data can be used to demonstrate the feasibility to use the virtual reality test in the epilepsy monitoring unit e.g. to monitor effects of seizures or medication on episodic memory.Yvonne Höller's research was funded by the Austrian Science Fund (FWF) : T 798-B27 and by the Research Fund of the Paracelsus Medical University : A-16/02/021-HÖL .Peer reviewe

Datasheets for Machine Learning Sensors

Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at the edge while empowering end-users with greater control of their data. As these ML sensors play a crucial role in the development of intelligent devices, clear documentation of their specifications, functionalities, and limitations is pivotal. This paper introduces a standard datasheet template for ML sensors and discusses its essential components including: the system's hardware, ML model and dataset attributes, end-to-end performance metrics, and environmental impact. We provide an example datasheet for our own ML sensor and discuss each section in detail. We highlight how these datasheets can facilitate better understanding and utilization of sensor data in ML applications, and we provide objective measures upon which system performance can be evaluated and compared. Together, ML sensors and their datasheets provide greater privacy, security, transparency, explainability, auditability, and user-friendliness for ML-enabled embedded systems. We conclude by emphasizing the need for standardization of datasheets across the broader ML community to ensure the responsible and effective use of sensor data

Lost in spatial translation - A novel tool to objectively assess spatial disorientation in Alzheimer's disease and frontotemporal dementia

Spatial disorientation is a prominent feature of early Alzheimer's disease (AD) attributed to degeneration of medial temporal and parietal brain regions, including the retrosplenial cortex (RSC). By contrast, frontotemporal dementia (FTD) syndromes show generally intact spatial orientation at presentation. However, currently no clinical tasks are routinely administered to objectively assess spatial orientation in these neurodegenerative conditions. In this study we investigated spatial orientation in 58 dementia patients and 23 healthy controls using a novel virtual supermarket task as well as voxel-based morphometry (VBM). We compared performance on this task with visual and verbal memory function, which has traditionally been used to discriminate between AD and FTD. Participants viewed a series of videos from a first person perspective travelling through a virtual supermarket and were required to maintain orientation to a starting location. Analyses revealed significantly impaired spatial orientation in AD, compared to FTD patient groups. Spatial orientation performance was found to discriminate AD and FTD patient groups to a very high degree at presentation. More importantly, integrity of the RSC was identified as a key neural correlate of orientation performance. These findings confirm the notion that i) it is feasible to assess spatial orientation objectively via our novel Supermarket task; ii) impaired orientation is a prominent feature that can be applied clinically to discriminate between AD and FTD and iii) the RSC emerges as a critical biomarker to assess spatial orientation deficits in these neurodegenerative conditions

Project-based, collaborative, algorithmic robotics for high school students: Programming self-driving race cars at MIT

We describe the pedagogy behind the MIT Beaver Works Summer Institute Robotics Program, a new high-school STEM program in robotics. The program utilizes state-of-the-art sensors and embedded computers for mobile robotics. These components are carried on an exciting 1/10-scale race-car platform. The program has three salient, distinguishing features: (i) it focuses on robotics software systems: the students design and build robotics software towards real-world applications, without being distracted by hardware issues; (ii) it champions project-based learning: the students learn through weekly project assignments and a final course challenge; (iii) the learning is implemented in a collaborative fashion: the students learn the basics of collaboration and technical communication in lectures, and they work in teams to design and implement their software systems. The program was offered as a four-week residential program at MIT in the summer of 2016. In this paper, we provide the details of this new program, its teaching objectives, and its results. We also briefly discuss future directions and opportunities