arXiv subjects

Liangyu Dong

Publications and source records attributed to Liangyu Dong.

3 recordsLinked to original sources

arXiv2026

Revisiting and Expanding the IPv6 Periphery: Global-Scale Measurement and Security Analysis

As IPv6 deployment accelerates, understanding the evolving security posture of network peripheries becomes increasingly important. A DSN 2021 study first explored IPv6 network peripheries, but its scope was limited to three regions and is now outdated. In this paper, we revisit and expand that work through a global-scale measurement and security analysis of IPv6 network peripheries. To support efficient large-scale scanning, we propose a novel Response-Guided Prefix Selection (RGPS) strategy to identify high-value IPv6 prefixes for probing. Our measurement covers 73 countries/regions and identifies over 281.9M active IPv6 network peripheries, including a 364.8% increase over the 2021 baseline in India, China, and America. We further analyze exposed services and find that 2.5% of devices expose at least one measured service, with recurring software vulnerabilities indicated by version-to-CVE mappings. As a case study of emerging exposed services, we design a Hierarchical LLM Exposure Verification (HLEV) framework to identify unauthorized-access risks in LLM deployment tools. Finally, we revisit routing loop vulnerabilities and identify 4.5M loop-prone devices, showing that flawed routing behaviors remain widespread. These findings indicate that although IPv6 adoption has surged, key security challenges persist across network peripheries.

cs.NI↗

arXiv2025

Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios

Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle to accommodate new task requirements at inference time. Addressing this typically requires costly data recollection and policy retraining for each new hardware or task configuration. To overcome this, we introduce an adaptation-projection strategy that enables a diffusion policy to perform zero-shot adaptation to novel manipulators and dynamic task settings, entirely at inference time and without any retraining. Our method first trains a diffusion policy in SE(3) space using demonstrations from a base manipulator. During online deployment, it projects the policy's generated trajectories to satisfy the kinematic and task-specific constraints imposed by the new hardware and objectives. Moreover, this projection dynamically adapts to physical differences (e.g., tool-center-point offsets, jaw widths) and task requirements (e.g., obstacle heights), ensuring robust and successful execution. We validate our approach on real-world pick-and-place, pushing, and pouring tasks across multiple manipulators, including the Franka Panda and Kuka iiwa 14, equipped with a diverse array of end-effectors like flexible grippers, Robotiq 2F/3F grippers, and various 3D-printed designs. Our results demonstrate consistently high success rates in these cross-manipulator scenarios, proving the effectiveness and practicality of our adaptation-projection strategy. The code will be released after peer review.

cs.RO↗

arXiv2025

Pick-and-place Manipulation Across Grippers Without Retraining: A Learning-optimization Diffusion Policy Approach

Current robotic pick-and-place policies typically require consistent gripper configurations across training and inference. This constraint imposes high retraining or fine-tuning costs, especially for imitation learning-based approaches, when adapting to new end-effectors. To mitigate this issue, we present a diffusion-based policy with a hybrid learning-optimization framework, enabling zero-shot adaptation to novel grippers without additional data collection for retraining policy. During training, the policy learns manipulation primitives from demonstrations collected using a base gripper. At inference, a diffusion-based optimization strategy dynamically enforces kinematic and safety constraints, ensuring that generated trajectories align with the physical properties of unseen grippers. This is achieved through a constrained denoising procedure that adapts trajectories to gripper-specific parameters (e.g., tool-center-point offsets, jaw widths) while preserving collision avoidance and task feasibility. We validate our method on a Franka Panda robot across six gripper configurations, including 3D-printed fingertips, flexible silicone gripper, and Robotiq 2F-85 gripper. Our approach achieves a 93.3% average task success rate across grippers (vs. 23.3-26.7% for diffusion policy baselines), supporting tool-center-point variations of 16-23.5 cm and jaw widths of 7.5-11.5 cm. The results demonstrate that constrained diffusion enables robust cross-gripper manipulation while maintaining the sample efficiency of imitation learning, eliminating the need for gripper-specific retraining. Video and code are available at https://github.com/yaoxt3/GADP.

cs.RO↗