Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on costly manual TOG annotations or only extract coarse grasping positions or regions from human demonstrations, limiting their practicality in real-world applications. To address these limitations, we introduce RTAGrasp, a Retrieval, Transfer, and Alignment framework inspired by human grasping strategies. Specifically, our approach first effortlessly constructs a robot memory from human grasping demonstration videos, extracting both TOG position and direction constraints. Then, given a task instruction and a visual observation of the target object, RTAGrasp retrieves the most similar human grasping experience from its memory and leverages semantic matching capabilities of vision foundation models to transfer the TOG constraints to the target object in a training-free manner. Finally, RTAGrasp aligns the transferred TOG constraints with the robot's action for execution. Evaluations on the public TOG benchmark, TaskGrasp dataset, show the competitive performance of RTAGrasp on both seen and unseen object categories compared to existing baseline methods. Real-world experiments further validate its effectiveness on a robotic arm. Our code, appendix, and video are available at https://sites.google.com/view/rtagrasp/home.

Human-robot interaction (HRI) encompasses a wide range of collaborative tasks, with handover being one of the most fundamental. As robots become more integrated into human environments, the potential for service robots to assist in handing objects to humans is increasingly promising. In robot-to-human (R2H) handover, selecting the optimal grasp is crucial for success, as it requires avoiding interference with the human's preferred grasp region and minimizing intrusion into their workspace. Existing methods either inadequately consider geometric information or rely on data-driven approaches, which often struggle to generalize across diverse objects. To address these limitations, we propose a novel zero-shot system that combines semantic and geometric information to generate optimal handover grasps. Our method first identifies grasp regions using semantic knowledge from vision-language models (VLMs) and, by incorporating customized visual prompts, achieves finer granularity in region grounding. A grasp is then selected based on grasp distance and approach angle to maximize human ease and avoid interference. We validate our approach through ablation studies and real-world comparison experiments. Results demonstrate that our system improves handover success rates and provides a more user-preferred interaction experience. Videos, appendixes and more are available at https://sites.google.com/view/vlm-handover.