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We introduce a fast and accurate language-driven grasp detection method using lightweight diffusion models. By integrating visual and textual features, our approach reduces inference time while achieving state-of-the-art performance. Real-world experiments validate its efficiency and effectiveness in robotic applications.

We present a novel approach for language-driven 6-DoF grasp detection in cluttered point clouds, introducing Grasp-Anything-6D, a large-scale dataset, and a diffusion model with negative prompt guidance to enable robots to grasp objects based on natural language commands, surpassing baselines in both benchmarks and real-world applications.

We present Grasp-Anything, a large-scale grasp detection dataset synthesized using foundation models to address the limited diversity of existing datasets. With 1M samples and over 3M objects, it enables zero-shot grasp detection and excels in vision-based and real-world robotic tasks. Code and data are available.

We propose a method for language-conditioned affordance detection and 6-DoF pose estimation in 3D point clouds, enabling robots to handle diverse affordances beyond predefined sets. Our approach features an open-vocabulary affordance detection branch and a language-guided diffusion model for pose generation. A new dataset supports the task, and experiments show significant performance improvements over baselines. The method demonstrates strong potential in real-world robotic applications.

We introduce an open-vocabulary affordance detection method for 3D point clouds, addressing the challenges of complex object shapes and diverse affordances. Using knowledge distillation and a novel text-point correlation approach, our method enhances feature extraction and semantic understanding. It outperforms baselines with a 7.96% mIOU improvement and supports real-time inference, ideal for robotic manipulation tasks.

Robot grasp detection is a complex challenge with significant industrial relevance. To address this, we present Grasp-Anything++, a new language-driven grasp detection dataset containing 1M samples, over 3M objects, and 10M grasping instructions. Leveraging foundation models, we frame grasp detection as a conditional generation task and propose a novel diffusion model-based method with a contrastive training objective to improve language-guided grasp pose detection. Our approach surpasses state-of-the-art methods, supports real-world robotic grasping, and enables zero-shot grasp detection. The dataset serves as a challenging benchmark, promoting advancements in language-driven robotic grasping research.

We propose a simple regression network to enhance intraoperative gamma activity visualization in endoscopic radio-guided cancer detection and resection. By leveraging high-dimensional image features and probe position data, our method effectively detects sensing areas, outperforming prior geometric approaches.

We propose SADepth, a self-supervised depth estimation method using Generative Adversarial Networks for laparoscopic imaging. Combining a multi-scale generator and adversarial learning, it overcomes limitations of photometric reprojection loss and improves generation quality. Experiments on public datasets demonstrate SADepth significantly outperforms state-of-the-art unsupervised methods, narrowing the gap with supervised approaches.