Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focus on imitating human hand trajectories, often neglecting the pivotal role of tool-environment interaction. This study addresses this gap by exploring the task of cucumber peeling as a case study to implement contact-based demonstration strategies in robotic tool use. Our approach concentrates on the subtle tool contact behaviors that manifest through contact dynamics. Specifically, we select appropriate tool stiffness for the peeling tasks, which is captured via a handheld teaching device equipped with optical tactile sensors. Subsequently, object-level stiffness control strategies are employed to emulate these behaviors using a three-fingered robotic hand. Experimental results from real-world cucumber peeling trials substantiate our methodology, illustrating that the robotic hand can adjust contact through finger movements, thereby achieving humanlike peeling efficiency without necessitating alterations to the tool structure. This study not only demonstrates the feasibility of sophisticated tool use by robotic hands, but also highlights the critical importance of integrating tactile feedback to refine interaction with the environment.

Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when significant distribution gaps exist between training and test data, while fine-tuning for diverse scenarios is often costly. Cache-based test-time adapters offer an efficient alternative by storing representative test samples to guide subsequent classifications. Yet, these methods typically employ naive cache management with limited capacity, leading to severe catastrophic forgetting when samples are inevitably dropped during updates. In this paper, we propose DOTA (DistributiOnal Test-time Adaptation), a simple yet effective method addressing this limitation. Crucially, instead of merely memorizing individual test samples, DOTA continuously estimates the underlying distribution of the test data stream. Test-time posterior probabilities are then computed using these dynamically estimated distributions via Bayes' theorem for adaptation. This distribution-centric approach enables the model to continually learn and adapt to the deployment environment. Extensive experiments validate that DOTA significantly mitigates forgetting and achieves state-of-the-art performance compared to existing methods.

In this paper, we present the TacShade: a newly designed 3D-printed soft optical tactile sensor. The sensor is developed for shape reconstruction under the inspiration of sketch drawing that uses the density of sketch lines to draw light and shadow, resulting in the creation of a 3D-view effect. TacShade, building upon the strengths of the TacTip, a single-camera tactile sensor of large in-depth deformation and being sensitive to edge and surface following, improves the structure in that the markers are distributed within the gap of papillae pins. Variations in light, dark and grey effects can be generated inside the sensor under the external contact interactions. The contours of the contacting objects are outlined by white markers, while the contact depth characteristics can be indirectly obtained from the distribution of black pins and white markers, creating a 2.5D visualization. Based on the imaging effect, we improve the Shape from Shading (SFS) algorithm to process tactile images, enabling a coarse but fast reconstruction for the contact objects. Two experiments are performed. The first verifies TacShade’s ability to reconstruct the shape of the contact objects through one image for object distinction. The second experiment shows the shape reconstruction capability of TacShade for a large panel with ridged patterns based on the location of robots and image splicing technology.

Distributed Denial of Service (DDoS) attacks threaten cloud servers by flooding redundant requests, leading to system resource exhaustion and legitimate service shutdown. Existing DDoS attack mitigation mechanisms mainly rely on resource expansion, which may result in unexpected resource over-provisioning and accordingly increase cloud system costs. To effectively mitigate DDoS attacks without consuming extra resources, the main challenges lie in the compromisesbetween incoming requests and available cloud resources. This paper proposes a resource-aware DDoS attack mitigation framework named RAM, where the mechanism of feedback in control theory is employed to adaptively adjust the interaction between incoming requests and available cloud resources. Specifically, two indicators including request confidence level and maximum cloud workload are designed. In terms of these two indicators, the incoming requests will be classified using proportional-integral-derivative (PID) feedback control-based classification scheme with request determination adaptation. The incoming requests can be subsequently processed according to their confidence levels as well as the workload and available resources of cloud servers, which achieves an effective resource-aware mitigation of DDoS attacks. Extensive experiments have been conducted to verify the effectiveness of RAM, which demonstrate that the proposed RAM can improve the request classification performance and guarantee the quality of service.