Humanoid robots encounter considerable difficulties in autonomously recovering from falls, especially within dynamic and unstructured environments. Conventional control methodologies are often inadequate in addressing the complexities associated with high-dimensional dynamics and the contact-rich nature of fall recovery. Meanwhile, reinforcement learning techniques are hindered by issues related to sparse rewards, intricate collision scenarios, and discrepancies between simulation and real-world applications. In this study, we introduce a multi-stage curriculum learning framework, termed HiFAR. This framework employs a staged learning approach that progressively incorporates increasingly complex and high-dimensional recovery tasks, thereby facilitating the robot’s acquisition of efficient and stable fall recovery strategies. Furthermore, it enables the robot to adapt its policy to effectively manage real-world fall incidents. We assess the efficacy of the proposed method using a real humanoid robot, showcasing its capability to autonomously recover from a diverse range of falls with high success rates, rapid recovery times, robustness, and generalization.

Hierarchical federated learning shows excellent potential for communication-computation trade-offs and reliable data privacy protection by introducing edge-cloud collaboration. Considering non-independent and identically distributed data distribution among devices and edges, this article aims to minimize the final loss function under time and energy budget constraints by optimizing the aggregation frequency and resource allocation jointly. Although there is no closed-form expression relating the final loss function to optimization variables, we divide the hierarchical federated learning process into multiple cloud intervals and analyze the convergence bound for each cloud interval. Then, we transform the initial problem into one that can be adaptively optimized in each cloud interval. We propose an adaptive hierarchical federated learning process, termed as AHFLP, where we determine edge and cloud aggregation frequency for each cloud interval based on estimated parameters, and then the CPU frequency of devices and wireless channel bandwidth allocation can be optimized in each edge. Simulations are conducted under different models, datasets and data distributions, and the results demonstrate the superiority of our proposed AHFLP compared with existing schemes.

Split Federated Learning (SFL) has emerged as a promising paradigm to enhance FL by partitioning the Machine Learning (ML) model into parts and deploying them across clients and servers, effectively mitigating the workload on resource-constrained devices and preserving privacy. Compared to cloud-device-based and edge-device-based SFL, cloud-edge-device collaborative SFL offers both lower communication latency and wider network coverage. However, existing works adopt a uniform model partitioning strategy for different devices, ignoring the heterogeneous nature of device resources. This oversight leads to severe straggler problems, making the training process inefficient. Moreover, they do not consider joint optimization of model aggregation control and computing and communication resource allocation, and lack distributed algorithm design. To address these issues, we propose a joint resource management scheme for cloud-edge-device collaborative SFL to optimize the training latency and energy consumption of all devices. In our scheme, the partitioning strategy is optimized for each device based on resource heterogeneity. Meanwhile, we jointly optimize the aggregation frequency of ML models, computing resource allocation for all devices and edge servers, and transmit power allocation for all devices. We formulate a coordination game among all edge servers and then design a distributed optimization algorithm employing partially observable Multi-Agent Deep Reinforcement Learning (MADRL) with integrated numerical methods. Extensive experiments are conducted to validate the convergence of our algorithm and demonstrate the superiority of our scheme via evaluations under multiple scenarios and in comparison with four reference schemes.

Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.