10 accepted papers
Prompt quality plays a critical role in the performance of the Segment Anything Model (SAM), yet existing approaches often rely on heuristic or manually crafted prompts, limiting scalability and generalization. In this paper, we propose Point Prompt Defender, an adversarial reinforcement learning fr
Robot-assisted needle insertion is widely employed in minimally invasive percutaneous interventions such as biopsy, brachytherapy, or ablation; however, most existing systems predominantly emphasize needle dexterity and manipulation within the soft tissue, neglecting the significant challenge of per
Tendon-driven continuum robots (TDCR) are widely used in medical interventions due to their inherent dexterity and compliance. However, precise motion planning and control of these robots remain challenging, largely because existing models do not accurately capture tendon frictional hysteresis. Pred
Optimizing prompts for foundation models like SAM represents a challenging high-dimensional black-box optimization problem, fundamentally plagued by the credit assignment ambiguity. To address this, we introduce PromptPilot, a task-agnostic reinforcement learning framework that structurally decompos…
Powered by extensive curated training data, the Segment Anything Model (SAM) demonstrates impressive generalization capabilities in open-world scenarios, effectively guided by user-provided prompts. However, the class-agnostic characteristic of SAM renders its segmentation accuracy highly dependent…
Concentric Tube Robots (CTRs) have been proposed to operate within the unstructured environment for minimally invasive surgeries. In this letter, we consider the operation scenario where the tubes travel inside the channels with a large clearance or large curvature, such as aortas or industrial pipe
Concentric Tube Robots (CTR) have the potential to enable effective minimally invasive surgeries. While extensive modeling and control work have been proposed in the past decade, limited efforts have been made to improve the path tracking performance from the perspective of manipulability, which can
Image rain removal requires the accurate separation between the pixels of the rain streaks and object textures. But the confusing appearances of rains and objects lead to the misunderstanding of pixels, thus remaining the rain streaks or missing the object details in the result. In this paper, we pr…
Shadow detection in single image has received signifi-cant research interests in recent years. However, much lessworks has been explored in shadow detection over dynamicscenes. The bottleneck is the lack of a well-establisheddataset with high-quality annotations for video shadow de-tection. In this…
Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirements. It might thus be desirable to offload intensive computation—not only sensing and planning, but also low-level whole-b…