We introduce a new task, called Class Incremental Robotic Pick-and-Place (CIRPAP), which calls for the capacity to learn to pick and place new categories of objects while retaining the skill of dealing with the previously learned ones. CIRPAP faces three challenges: catastrophic forgetting, few-shot learning, and robust picking in cluttered environments. To address the challenges of catastrophic forgetting and few-shot learning, we propose a novel CIRPAP framework that is built on Incremental Few-Shot Object Detection (iFSD). Specifically, with fixed pre-trained Transformerlike object detection models, we only fine-tune the additional adapter modules, which is called adapter-tuning. To address the challenge of robust picking in cluttered environments, we also utilize multiview fusion to integrate object detection and grasp prediction results. As for iFSD evaluation, experiments show that our adapter-tuning-based approach outperforms state-of-the-art methods on COCO and our dataset. As for full CIRPAP system evaluation, experimental results on a real robotic platform demonstrate the effectiveness of our proposed framework.