Researchers have developed a system for robotic pick-and-place tasks using annotation-guided visual prompting. The approach uses bounding box annotations to identify objects and placement locations, providing structured spatial guidance. Instead of traditional step-by-step planning, the system employs Action Chunking with Transformers (ACT) as an imitation learning algorithm. This enables the robotic arm to predict chunked action sequences from human demonstrations, facilitating smooth and adaptive pick-and-place operations. --- Why it matters: This work matters because it addresses a significant challenge in robotics: adapting to complex environments with varying object properties. The proposed system's ability to learn from human demonstrations and adapt to new situations could improve the efficiency and reliability of robotic pick-and-place tasks. Source: https://arxiv.org/abs/2508.08748