Mobile edge computing (MEC) is a promising paradigm that provides abundant computation and storage resources at the edge close to mobile devices (MDs). In MEC networks, MDs offload compute-heavy tasks to nearby edge servers (ESs) for delay-sensitive processing, where relevant services are stored to support task execution. However, the limited computation and storage capacities of ESs make joint optimization of service caching and computation offloading challenging due to coupled decisions, a large solution space, and dynamic environments. In this paper, we investigate the joint optimization of service caching and computation offloading in MEC networks, aiming to maximize the cache hit ratio and minimize the average service latency. To tackle this problem, the original formulation is decomposed into two hierarchical subproblems, namely high-level service caching and low-level computation offloading. We propose a novel hierarchical deep reinforcement learning (DRL) algorithm with active inference, termed HADRL. At the high-level, we adopt a deep deterministic policy gradient (DDPG) based DRL approach to maximize the cache hit ratio. At the low-level, we employ an active inference based DRL approach to minimize the average service latency. Unlike conventional DRL, the active inference based DRL approach selects policies by minimizing expected free energy instead of relying only on explicit rewards, making it well suited for highly dynamic low-level computation offloading. According to the simulation outcomes, the HADRL scheme surpasses the benchmark algorithms with respect to cache hit ratio as well as average service latency.

As healthcare systems evolve and healthcare data grows, the need for cross-domain collaboration treatment has become more complex, necessitating fine-grained access control to enhance privacy and security. Blockchain provides a distributed trusted platform without third parties, but the current blockchain-based access control systems lack efficiency and sufficient privacy protection in cross-domain collaboration. To address these challenges, we propose SWIFTGUARD, an efficient and fine-grained access control system based on a master-slave chain to strengthen the security and privacy of cross-domain healthcare collaboration. SWIFTGUARD incorporates a zero-knowledge proof protocol for cross-domain authentication with-out exposing sensitive data and leverages quantitative attribute weights for efficient access control. Through game-based security proof, we demonstrate the zero knowledge and soundness of the system. Extensive experiments evaluate that SWIFTGUARD reduces the time complexity of access authorization from O($n$) to O(log$n$), with improved throughput and stable performance in cross-domain collaboration. Our comprehensive evaluation confirms that SWIFTGUARD provides a secure and efficient access control system for cross-domain healthcare collaboration.

Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often limited by the quantity quality, and diversity of demonstrations. This paper explores improving offline-trained imitation learning models through online interactions with the environment. We introduce Policy Decorator, which uses a model-agnostic residual policy to refine large imitation learning models during online interactions. By implementing controlled exploration strategies, Policy Decorator enables stable, sample-efficient online learning. Our evaluation spans eight tasks across two benchmarks—ManiSkill and Adroit—and involves two state-of-the-art imitation learning models (Behavior Transformer and Diffusion Policy). The results show Policy Decorator effectively improves the offline-trained policies and preserves the smooth motion of imitation learning models, avoiding the erratic behaviors of pure RL policies. See our [project page](https://policydecorator.github.io/) for videos.

With the increasing integration of robots into human life, their role in architectural spaces where people spend most of their time has become more prominent. While motion capabilities and accurate localization for automated robots have rapidly developed, the challenge remains to generate efficient, smooth, comprehensive, and high-quality trajectories in these areas. In this paper, we propose a novel efficient planner for ground robots to autonomously navigate in large complex multi-layered architectural spaces. Considering that traversable regions typically include ground, slopes, and stairs, which are planar or nearly planar structures, we simplify the problem to navigation within and between complex intersecting planes. We first extract traversable planes from 3D point clouds through segmenting, merging, classifying, and connecting to build a plane-graph, which is lightweight but fully represents the traversable regions. We then build a trajectory optimization based on motion state trajectory and fully consider special constraints when crossing multi-layer planes to maximize the robot's maneuverability. We conduct experiments in simulated environments and test on a CubeTrack robot in real-world scenarios, validating the method's effectiveness and practicality.

Terrain analysis is critical for the practical application of ground mobile robots in real-world tasks, especially in outdoor unstructured environments. In this paper, we propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to effectively navigate through complex terrains. Our approach utilizes sparse Gaussian processes (SGP) to extract geometric features (curvature, gradient, elevation, etc.) directly from point cloud scans. These features are then used to construct a high-resolution local traversability map. Then, we design a spatial-temporal Bayesian Gaussian kernel (BGK) inference method to dynamically evaluate traversability scores, integrating historical and real-time data while considering factors such as slope, flatness, gradient, and uncertainty metrics. GPU acceleration is applied in the feature extraction step, and the system achieves real-time performance. Extensive simulation experiments across diverse terrain scenarios demonstrate that our method outperforms SOTA approaches in both accuracy and computational efficiency. Additionally, we develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor environments. Our code will be open-source for further research and development by the community, https://github.com/ZJU-FAST-Lab/FSGP_BGK.

Existing robotic trajectory planning frameworks typically approximate the robot’s geometry and environmental constraints. While this improves computational efficiency, it sacrifices the solution space and frequently encounters failure in confined environments. However, attaining a precise geometric representation and a continuous collision-free trajectory usually necessitates greater computational expenditure. This paper proposes a methodology that utilizes the concept of swept volume to address the identified limitations. The implementation of an efficient Swept Volume Signed Distance Field computation algorithm and a B-spline trajectory representation results in a significant increase in computational efficiency while maintaining strict safety guarantees. The proposed method combines the advantages of efficiency and maximal exploitation of the solution space. Additionally, it ensures continuous obstacle avoidance, achieving real-time 10Hz replanning performance on i50000 NUC11TNK for arbitrarily shaped rigid objects in complex, unstructured environments.

Differential drive robots are widely used in various scenarios thanks to their straightforward principle, from household service robots to disaster response field robots. The nonholonomic dynamics and possible lateral slip of these robots lead to difficulty in getting feasible and high-quality trajectories. Although there are several types of driving mechanisms for real-world applications, they all share a similar driving principle, which involves controlling the relative motion of independently actuated tracks or wheels to achieve both linear and angular movement. Therefore, a comprehensive trajectory optimization to compute trajectories efficiently for various kinds of differential drive robots is highly desirable. In this paper, we propose a universal trajectory optimization framework, enabling the generation of high-quality trajectories within a restricted computational timeframe for these robots. We introduce a novel trajectory representation based on polynomial parameterization of motion states or their integrals, such as angular and linear velocities, which inherently matches the robots’ motion to the control principle. The trajectory optimization problem is formulated to minimize computation complexity while prioritizing safety and operational efficiency. We then build a full-stack autonomous planning and control system to demonstrate its feasibility and robustness. We conduct extensive simulations and real-world testing in crowded environments with three kinds of differential drive robots to validate the effectiveness of our approach.Note to Practitioners—The Differential drive robot, known for its simple mechanics and high maneuverability, is widely used in many applications. However, current methods have limitations in practice when high-performance motion is needed. Due to the state representation in Cartesian space, path planning makes it difficult to consider nonholonomic constraints directly. The existing trajectory optimization cannot effectively constrain the angular velocity and it is difficult to model forward and backward motion into a continuous trajectory. This paper provides a novel trajectory representation that inherently utilizes the motion performance of differential drive robots, which ensures its universality for different platforms, and reduces the time required to generate trajectories to ensure real-time performance. Based on this, we propose a robust planning and control framework to achieve efficient navigation. We release the source code athttps://zju-fast-lab.github.io/DDR-opt/facilitating expansion and deployment for practitioners. We validate this framework through extensive experiments, demonstrating its capability to navigate challenging environments.

Tracked robots with reconfigurable mechanisms exhibit great maneuverability due to their adaptability to complex ground conditions. Reconfigurable tracked robots with geometry-changing tracks show further obstacle-crossing capabilities with compact dimensions. However, existing systems face deployment limitations due to either complex transmission mechanisms or unsustainable designs when maintaining the tension in the tracks. To address these challenges, we introduce a novel design of a reconfigurable tracked robot with geometry-changing tracks, which achieves strong terrain traversability with good mechanical properties. We achieve the elliptical trajectory of key planetary wheels through a novel Quad-slider Elliptical Trammel Mechanism (Qs-ETM), allowing the tracks to maintain fixed tension while changing their geometry. Furthermore, the combination of direct drive motors significantly enhances its mechanical properties and agility. A detailed analysis of the kinematic and dynamic characteristics has been conducted and proved with a series of simulations. We built a fully functional prototype of the design and tested it in real-world experiments to validate its advantages. The result shows that our design can reduce the torque required by up to 68.3% and the shear stress of the flipper by up to 67.1%.

Service robots have attracted extensive attention due to specially designed functions, such as mobile manipulators or robots with extra structures. For robots that have changing shapes, autonomous navigation in the real world presents new challenges. In this paper, we propose a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments. We model the whole-body trajectory as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of the extra joints. These constraints are converted into soft constraints, and an activation function for dense sampling is applied to avoid nonlinear mutations. In addition, we guarantee the safety of full shape by limiting the system's distance from obstacles. To comprehensively simulate a large extent of height and width changes, we designed a novel Shape-Changing Robot with a Differential Base (SCR-DB). Our global trajectory optimization gives a smooth and collision-free trajectory for SCR-DB at a low computational cost. We present vast simulations and real-world experiments to validate our performance, including coupled whole-body and independent differential-driven vehicle motion planning.

Many deep learning methods have been proposed for the diagnosis of COVID-19 since the global pandemic. However, few studies have focused on the disease course classification of COVID-19, which is crucial for radiologists to determine treatment plans. This paper proposes a Multi-Modal Fusion Cascade (MMFC) framework for this task, which can make the most of multi-modal information, including CT image and bio-information (laboratory examination, clinical characterization, etc.). The proposed framework consists of two parts: Bio-Visual Feature Learning Module (BFL) and Joint Decision Module (JD). Firstly, BFL learns the discriminative visual features from the mediastinal window with the assistance of bio-information. According to the official Treatment Protocol of China, the bio-information is chosen and helps the BFL better extract the images’ bio-visual features and then obtained a disease course classification result based on CT images. Secondly, JD uses bio-information again and fuses the confidence of BFL’s result to make the joint decision. Experimental results show that our framework significantly improves accuracy and sensitivity compared to the baseline.