Yuxing Han

Zhitao Wang, Yanke Wang, Jiangtao Wen et al.

Robotic manipulation in unstructured environments requires the generation of robust and long-horizon trajectory-level policy with conditions of perceptual observations and benefits from the advantages of SE(3)-equivariant diffusion models that are data-efficient. However, these models suffer from the inference time costs. Inspired by the inference efficiency of rectified flows, we introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, i.e., Rectifying SE(3)-Equivariant Policy Learning Flows, providing fast, geodesic-consistent, least-computational policy generation. Crucially, both components employ SE(3)-equivariant networks to preserve rotational and translational symmetry, enabling robust generalization under rigid-body motions. With the verification on the simulated benchmarks, we find that the proposed ReSeFlow with only one inference step can achieve better performance with lower geodesic distance than the baseline methods, achieving up to a 48.5% error reduction on the painting task and a 21.9% reduction on the rotating triangle task compared to the baseline's 100-step inference. This method takes advantages of both SE(3) equivariance and rectified flow and puts it forward for the real-world application of generative policy learning models with the data and inference efficiency.

Zhitao Wang, Yirong Xiong, Roberto Horowitz et al.

Rebar tying is a repetitive but critical task in reinforced concrete construction, typically performed manually at considerable ergonomic risk. Recent advances in robotic manipulation hold the potential to automate the tying process, yet face challenges in accurately estimating tying poses in congested rebar nodes. In this paper, we introduce a hybrid perception and motion planning approach that integrates geometry-based perception with Equivariant Denoising Diffusion on SE(3) (Diffusion-EDFs) to enable robust multi-node rebar tying with minimal training data. Our perception module utilizes density-based clustering (DBSCAN), geometry-based node feature extraction, and principal component analysis (PCA) to segment rebar bars, identify rebar nodes, and estimate orientation vectors for sequential ranking, even in complex, unstructured environments. The motion planner, based on Diffusion-EDFs, is trained on as few as 5-10 demonstrations to generate sequential end-effector poses that optimize collision avoidance and tying efficiency. The proposed system is validated on various rebar meshes, including single-layer, multi-layer, and cluttered configurations, demonstrating high success rates in node detection and accurate sequential tying. Compared with conventional approaches that rely on large datasets or extensive manual parameter tuning, our method achieves robust, efficient, and adaptable multi-node tying while significantly reducing data requirements. This result underscores the potential of hybrid perception and diffusion-driven planning to enhance automation in on-site construction tasks, improving both safety and labor efficiency.

Jie Wang, Chen Ye Gan, Caoqi Wei et al.

Feature matching across video streams remains a cornerstone challenge in computer vision. Increasingly, robust multimodal matching has garnered interest in robotics, surveillance, remote sensing, and medical imaging. While traditional rely on detecting and matching spatial features, they break down when faced with noisy, misaligned, or cross-modal data. Recent deep learning methods have improved robustness through learned representations, but remain constrained by their dependence on extensive training data and computational demands. We present Flow Intelligence, a paradigm-shifting approach that moves beyond spatial features by focusing on temporal motion patterns exclusively. Instead of detecting traditional keypoints, our method extracts motion signatures from pixel blocks across consecutive frames and extract temporal motion signatures between videos. These motion-based descriptors achieve natural invariance to translation, rotation, and scale variations while remaining robust across different imaging modalities. This novel approach also requires no pretraining data, eliminates the need for spatial feature detection, enables cross-modal matching using only temporal motion, and it outperforms existing methods in challenging scenarios where traditional approaches fail. By leveraging motion rather than appearance, Flow Intelligence enables robust, real-time video feature matching in diverse environments.