Large Language Models (LLMs) have shown the promising planning capabilities for robotic manipulation, which advances the development of embodied intelligence significantly. However, existing LLM-driven robotic manipulation approaches excel at simple pick-and-place tasks but are insufficient for complex manipulation tasks due to inaccurate procedural knowledge. Besides, for embodied intelligence, equipping a large scale LLM is energy-consuming and inefficient, which affects its real-world application. To address the above problems, we propose Hierarchical Procedural Knowledge Graphs (\textbf{HP-KG}) to enhance LLMs for complex robotic planning while significantly reducing the demand for LLM scale in robotic manipulation. Considering that the complex real-world tasks require multiple steps, and each step is composed of robotic-understandable atomic actions, we design a hierarchical knowledge graph structure to model the relationships between tasks, steps, and actions. This design bridges the gap between human instructions and robotic manipulation actions. To construct HP-KG, we develop an automatic knowledge graph construction framework powered by LLM-based multi-agents, which eliminates costly manual efforts while maintaining high-quality graph structures. The resulting HP-KG encompasses over 40k activity steps across more than 6k household tasks, spanning diverse everyday scenarios. Extensive experiments demonstrate that small scale LLMs (7B) enhanced by our HP-KG significantly improve the planning capabilities, which are stronger than 72B LLMs only. Encouragingly, our approach remains effective on the most powerful GPT-4o model.

Instance segmentation in medical imaging plays a crucial role in clinical diagnostic tasks, and have shown promising performance in practical applications. In this paper, we discuss a more fine-grained instance segmentation task: dental structured instance segmentation based on panoramic radiographs. However, direct segmentation of tooth structures encounters inherent challenges. Traditional instance segmentation networks often fall short in capturing intricate internal features, and exacerbated by the frequent blurring found in medical imaging, which can result in the deficiency of anatomical details. To deal with these problems, we propose a novel framework called DSISDPR, which combines a dental structured instance segmentation (DSIS) network with an enhanced diffusion prior refinement (DPR) method. Specifically, our innovatively designed structureaware network leverages fine-grained feature fusion, acquiring a richer representation of internal anatomical structures. With the integration of adversarial learning, the model is primed to deliver holistic and subtle predictions of tooth structures. Furthermore, taking inspiration from dentists’ inherent ability to utilize prior knowledge, such as understanding dental structures to label invisible anatomical structures, we propose a diffusion inpainting to refine the results of DSIS without additional annotations. Equipped with built-in structure learning, DPR is capable of modifying anomalies within each predicted segmentation, resulting in a more robust and complete structured segmentation result. Meanwhile, we ensure rigorous oversight over the reconstruction of areas affected by abnormalities, ensuring that any introduced adjustments minimally disrupt the wellpredicted structured segmentation results. Extensive experiments have demonstrated that our DSIS-DPR outperforms all existing classical instance segmentation networks. The collected dataset is available:https://github.com/Zzz512/TSD.