Longhua Ma

Yuhang Dong, Haizhou Ge, Yupei Zeng et al.

Visuomotor imitation learning policies enable robots to efficiently acquire manipulation skills from visual demonstrations. However, as scene complexity and visual distractions increase, policies that perform well in simple settings often experience substantial performance degradation. To address this challenge, we propose ImitDiff, a diffusion-based imitation learning policy guided by fine-grained semantics within a dual-resolution workflow. Leveraging pretrained priors of vision-language foundation models, our method transforms high-level instructions into pixel-level visual semantic masks. These masks guide a dual-resolution perception pipeline that captures both global context (e.g., overall layout) from low-resolution observation and fine-grained local features (e.g., geometric details) from high-resolution observation, enabling the policy to focus on task-relevant regions. Additionally, we introduce a consistency-driven diffusion transformer action head that bridges visual semantic conditions and real-time action generation. Extensive experiments demonstrate that ImitDiff outperforms state-of-the-art vision-language manipulation frameworks, as well as visuomotor imitation learning policies, particularly under increased scene complexity and visual distractions. Notably, ImitDiff exhibits strong generalization in zero-shot settings involving novel objects and visual distractions. Furthermore, our consistency-driven action head achieves an order-of-magnitude improvement in inference speed while maintaining competitive success rates.

Zhiwei Wang, Jun Huang, Longhua Ma et al.

In visual tasks, large teacher models capture essential features and deep information, enhancing performance. However, distilling this information into smaller student models often leads to performance loss due to structural differences and capacity limitations. To tackle this, we propose a distillation framework based on graph knowledge, including a multi-level feature alignment strategy and an attention-guided mechanism to provide a targeted learning trajectory for the student model. We emphasize spectral embedding (SE) as a key technique in our distillation process, which merges the student's feature space with the relational knowledge and structural complexities similar to the teacher network. This method captures the teacher's understanding in a graph-based representation, enabling the student model to more accurately mimic the complex structural dependencies present in the teacher model. Compared to methods that focus only on specific distillation areas, our strategy not only considers key features within the teacher model but also endeavors to capture the relationships and interactions among feature sets, encoding these complex pieces of information into a graph structure to understand and utilize the dynamic relationships among these pieces of information from a global perspective. Experiments show that our method outperforms previous feature distillation methods on the CIFAR-100, MS-COCO, and Pascal VOC datasets, proving its efficiency and applicability.