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Stereo disparity estimation is crucial for obtaining depth information in robot-assisted minimally invasive surgery (RAMIS). We propose the StereoMamba architecture, which is specifically designed for stereo disparity estimation in RAMIS. Our approach is based on a novel Feature Extraction Mamba (FE-Mamba) module, which enhances long-range spatial dependencies both within and across stereo images.

Language-driven grasp detection has the potential to revolutionize human-robot interaction by allowing robots to understand and execute grasping tasks based on natural language commands. In this paper, we introduce GraspMAS, a new multi-agent system framework for language-driven grasp detection. GraspMAS is designed to reason through ambiguities and improve decision-making in real-world scenarios.

Endovascular navigation is a crucial aspect of minimally invasive procedures, where precise control of curvilinear instruments like guidewires is critical for successful interventions. We propose SplineFormer, a new transformer-based architecture, designed specifically to predict the continuous, smooth shape of the guidewire in an explainable way.

Vision language models have played a key role in extracting meaningful features for various robotic applications. In this paper, we introduce Robotic-CLIP to enhance robotic perception capabilities.

Accurate tracking of tissues and instruments in videos is crucial for Robotic-Assisted Minimally Invasive Surgery. We introduce a new annotated surgical tracking dataset for benchmarking tracking methods for surgical scenarios, comprising real-world surgical videos with complex tissue and instrument motions.

In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. This paper proposes a new method to train a foundation model in a decentralized federated learning setting for endovascular intervention.

This paper presents a learning-based method to realize the autonomous radiotracer detection in robot-assisted surgeries by navigating the probe to the radioactive target. Real-world evaluation on the da Vinci Research Kit (dVRK) further confirms the feasibility of the approach, achieving an 80% success rate in radiotracer detection.

Accurate 3D reconstruction of dynamic surgical scenes from endoscopic video is essential for robotic-assisted surgery. We present SurgicalGS, a dynamic 3D Gaussian Splatting framework specifically designed for surgical scene reconstruction with improved geometric accuracy.

We present HabiCrowd, a benchmark for crowd-aware visual navigation, integrating diverse human dynamics into photorealistic environments. HabiCrowd achieves state-of-the-art collision avoidance and superior computational efficiency, advancing studies in human-robot interaction and navigation.

We propose a novel method for language-driven grasp detection using mask-guided attention and transformer mechanisms with semantic segmentation features. By integrating visual data and natural language, our approach achieves a 10% improvement in grasp detection accuracy and excels in real-world robotic experiments.