This paper develops a hierarchical learning and optimization framework that can learn and achieve well-coordinated multi-skill locomotion. The learned multi-skill policy can switch between skills automatically and naturally in tracking arbitrarily positioned goals and recover from failures promptly. The proposed framework is composed of a deep reinforcement learning process and an optimization process. First, the contact pattern is incorporated into the reward terms for learning different types of gaits as separate policies without the need for any other references. Then, a higher level policy is learned to generate weights for individual policies to compose multi-skill locomotion in a goal-tracking task setting. Skills are automatically and naturally switched according to the distance to the goal. The proper distances for skill switching are incorporated in reward calculation for learning the high level policy and updated by an outer optimization loop as learning progresses. We first demonstrated successful multi-skill locomotion in comprehensive tasks on a simulated Unitree A1 quadruped robot. We also deployed the learned policy in the real world showcasing trotting, bounding, galloping, and their natural transitions as the goal position changes. Moreover, the learned policy can react to unexpected failures at any time, perform prompt recovery, and resume locomotion successfully. Compared to discrete switch between single skills which failed to transition to galloping in the real world, our proposed approach achieves all the learned agile skills, with smoother and more continuous skill transitions.

Simple and effective interaction between human and quadrupedal robots paves the way towards creating intelligent and capable helper robots, forging a future where technology enhances our lives in ways beyond our imagination [1, 2, 3]. Key to such human-robot interaction system is enabling quadrupedal robots to respond to natural language instructions as language is one of the most important communication channels for human beings. Recent developments in Large Language Models (LLMs) have engendered a spectrum of applications that were once considered unachievable, including virtual assistance [4], code generation [5], translation [6], and logical reasoning [7], fueled by the proficiency of LLMs to ingest an enormous amount of historical data, to adapt in-context to novel tasks with few examples, and to understand and interact with user intentions through a natural language interface. The burgeoning success of LLMs has also kindled interest within the robotics researcher community, with an aim to develop interactive and capable systems for physical robots [8, 9, 10, 11, 12, 13]. Researchers have demonstrated the potential of using LLMs to perform high-level planning [8, 9], and robot code writing [11, 13]. Nevertheless, unlike text generation where LLMs directly interpret the atomic elements--tokens--it often proves challenging for LLMs to comprehend low-level robotic commands such as joint angle targets or motor torques, especially for inherently unstable legged robots necessitating high-frequency control signals. Consequently, most existing work presume the provision of high-level APIs for LLMs to dictate robot behaviour, inherently limiting the system's expressive capabilities. We address this limitation by using foot contact patterns as an interface that bridges human instructions in natural language and low-level commands.

Contact planning is crucial in locomoting systems.Specifically, appropriate contact planning can enable versatile behaviors (e.g., sidewinding in limbless locomotors) and facilitate speed-dependent gait transitions (e.g., walk-trot-gallop in quadrupedal locomotors). The challenges of contact planning include determining not only the sequence by which contact is made and broken between the locomotor and the environments, but also the sequence of internal shape changes (e.g., body bending and limb shoulder joint oscillation). Most state-of-art contact planning algorithms focused on conventional robots (e.g.biped and quadruped) and conventional tasks (e.g. forward locomotion), and there is a lack of study on general contact planning in multi-legged robots. In this paper, we show that using geometric mechanics framework, we can obtain the global optimal contact sequence given the internal shape changes sequence. Therefore, we simplify the contact planning problem to a graph optimization problem to identify the internal shape changes. Taking advantages of the spatio-temporal symmetry in locomotion, we map the graph optimization problem to special cases of spin models, which allows us to obtain the global optima in polynomial time. We apply our approach to develop new forward and sidewinding behaviors in a hexapod and a 12-legged centipede. We verify our predictions using numerical and robophysical models, and obtain novel and effective locomotion behaviors.

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between sub-groups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements - locations under-served by epidemic models in general. In this paper we present a novel, mixed-method approach, for deriving contact matrices in populations which combines a lightweight, rapidly deployable, survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. The matrices from the refugee settlement show strong banding effects due to different age cut-offs in attendance at certain venues, such as distribution centres and religious sites, as well as the important contribution of the demographic profile of the settlement which was encoded in the model. These can have significant implications to the modelled disease dynamics. To validate our approach, we also apply our method to the population of the UK and compare our derived matrices against well-known contact matrices previously collected using traditional approaches. Overall, our findings demonstrate that our mixed-method approach can address some of the challenges of both the traditional and previously proposed agent-based approaches to deriving contact matrices, and has the potential to be rolled-out in other resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and IDP settlements and better serving these vulnerable communities.

We create a virtual 2D tactile array for soft pneumatic actuators using embedded audio components. We detect contact-specific changes in sound modulation to infer tactile information. We evaluate different sound representations and learning methods to detect even small contact variations. We demonstrate the acoustic tactile sensor array by the example of a PneuFlex actuator and use a Braille display to individually control the contact of 29x4 pins with the actuator's 90x10 mm palmar surface. Evaluating the spatial resolution, the acoustic sensor localizes edges in x- and y-direction with a root-mean-square regression error of 1.67 mm and 0.0 mm, respectively. Even light contacts of a single Braille pin with a lifting force of 0.17 N are measured with high accuracy. Finally, we demonstrate the sensor's sensitivity to complex contact shapes by successfully reading the 26 letters of the Braille alphabet from a single display cell with a classification rate of 88%.

How can the resurgent epidemics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during 2020 be explained? Are they a result of students going back to school? To address this question, Monod et al. created a contact matrix for infection based on data collected in Europe and China and extended it to the United States. Early in the pandemic, before interventions were widely implemented, contacts concentrated among individuals of similar age were the highest among school-aged children, between children and their parents, and between middle-aged adults and the elderly. However, with the advent of nonpharmaceutical interventions, these contact patterns changed substantially. By mid-August 2020, although schools reopening facilitated transmission, the resurgence in the United States was largely driven by adults 20 to 49 years of age. Thus, working adults who need to support themselves and their families have fueled the resurging epidemics in the United States. Science , this issue p. [eabe8372][1] ### INTRODUCTION After initial declines, in mid-2020, a sustained resurgence in the transmission of novel coronavirus disease (COVID-19) occurred in the United States. Throughout the US epidemic, considerable heterogeneity existed among states, both in terms of overall mortality and infection, but also in the types and stringency of nonpharmaceutical interventions. Despite these stark differences among states, little is known about the relationship between interventions, contact patterns, and infections, or how this varies by age and demographics. A useful tool for studying these dynamics is individual, age-specific mobility data. In this study, we use detailed mobile-phone data from more than 10 million individuals and establish a mechanistic relationship between individual contact patterns and COVID-19 mortality data. ### RATIONALE As the pandemic progresses, disease control responses are becoming increasingly nuanced and targeted. Understanding fine-scale patterns of how individuals interact with each other is essential to mounting an efficient public health control program. For example, the choice of closing workplaces, closing schools, limiting hospitality sectors, or prioritizing vaccination to certain population groups should be informed by the demographics currently driving and sustaining transmission. To develop the tools to answer such questions, we introduce a new framework that links mobility to mortality through age-specific contact patterns and then use this rich relationship to reconstruct accurate transmission dynamics (see figure panel A). ### RESULTS We find that as of 29 October 2020, adults aged 20 to 34 and 35 to 49 are the only age groups that have sustained SARS-CoV-2 transmission with reproduction numbers (transmission rates) consistently above one. The high reproduction numbers from adults are linked both to rebounding mobility over the summer and elevated transmission risks per venue visit among adults aged 20 to 49. Before school reopening, we estimate that 75 of 100 COVID-19 infections originated from adults aged 20 to 49, and the share of young adults aged 20 to 34 among COVID-19 infections was highly variable geographically. After school reopening, we reconstruct relatively modest shifts in the age-specific sources of resurgent COVID-19 toward younger individuals, with less than 5% of SARS-CoV-2 transmissions attributable to children aged 0 to 9 and less than 10% attributable to early adolescents and teenagers aged 10 to 19. Thus, adults aged 20 to 49 continue to be the only age groups that contribute disproportionately to COVID-19 spread relative to their size in the population (see figure panel B). However, because children and teenagers seed infections among adults who are more transmission efficient, we estimate that overall, school opening is indirectly associated with a 26% increase in SARS-CoV-2 transmission. ### CONCLUSION We show that considering transmission through the lens of contact patterns is fundamental to understanding which population groups are driving disease transmission. Over time, the share of age groups among reported deaths has been markedly constant, and the data provide no evidence that transmission shifted to younger age groups before school reopening, and no evidence that young adults aged 20 to 34 were the primary source of resurgent epidemics since the summer of 2020. Our key conclusion is that in locations where novel, highly transmissible SARS-CoV-2 lineages have not yet become established, additional interventions among adults aged 20 to 49, such as mass vaccination with transmission-blocking vaccines, could bring resurgent COVID-19 epidemics under control and avert deaths. ![Figure][2] Model developed to estimate the contribution of age groups to resurgent COVID-19 epidemics in the United States. ( A ) Model overview. ( B ) Estimated contribution of age groups to SARS-CoV-2 transmission in October. After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions—including transmission-blocking vaccines—to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19–attributable deaths. [1]: /lookup/doi/10.1126/science.abe8372 [2]: pending:yes