The use of cables in aerial manipulation offers a lightweight and flexible alternative to rigid grasping mechanisms. However, achieving autonomous tying and secure transportation of objects with cables remains a significant challenge. In this work, we present a novel method for autonomously securing and transporting objects using multi-layer hitches formed in midair by a team of aerial robots. Building upon prior work on polygonal hitch formation, we extend the framework to include layered cable interlacing that increases frictional grip and enables secure object grasping. We introduce two new manipulation actions: multi-layer tying and autonomous object release, completing the pipeline for aerial grasping and transportation. We develop a capstan-based analytical model that establishes an exponential scaling law for the effect of cable layers and provides a conservative guideline. The formation algorithm operates in parallel, ensuring scalability to large teams with constant execution time. We validate our system through simulation and hardware experiments, demonstrating fully autonomous object tying, lifting, and releasing using cables alone without human intervention.

Diffusion models are increasingly employed in visuomotor policies to achieve promising performance of behavior cloning. However, the slow inference caused by iterative denoising is a notorious disadvantage, which greatly limits its application in resource-limited and real-time interactive robot systems. The prevailing strategy to this problem is distillation, but it still requires considerable resources to retrain a student model. To this end, we take another training-free view to develop a novel Fast Policy (termed FP), which can be regarded as a powerful and accelerated alternative to Diffusion Policy for learning visuomotor robot control. Specifically, our comprehensive study of UNet encoder shows that its features change little during inference, prompting us to reuse encoder features in non-critical denoising steps. In addition, we design strategies based on Fourier energy to screen critical and non-critical steps dynamically according to different tasks. Importantly, to mitigate performance degradation caused by the repeated use of non-critical steps, we further introduce a noise correction strategy. Our FP is evaluated on multiple simulation benchmarks and the comparison results with existing speed-up methods demonstrate our effectiveness and superiority with state-of-the-art success rates in visuomotor inference speed. The code is available at https://github.com/xwccchong/Fast-Policy

Hyperspectral unmixing (HU) aims to decompose mixed pixels into their constituent spectral signatures and estimate their corresponding fractional abundances. Recently, the nonlinear spectral mixing model (NLMM) has advanced significantly and offered strong physical interpretability. However, the effective integration of physics-driven NLMM with data-driven deep learning (DL) approaches still remains a critical challenge. To address this, we propose a diffusion-enhanced autoencoder (DEAE), a novel unsupervised framework that innovatively incorporates the diffusion model (DM) into nonlinear HU. DEAE introduces the residual second-order attention mechanism to capture global spectral information, adaptively weighting informative bands while compressing redundant bands. Subsequently, we integrate the extended multilinear mixing model (EMLM) into the DM-enhanced decoder, which extracts latent features from the linear autoencoder’s output and generates an enhanced reconstructed image while simultaneously estimating the transition probabilities of EMLM. Finally, a nonlinear decoder outputs the ultimate reconstructed image based on both the enhanced reconstructed image and transition probabilities. Experiments conducted on synthetic and three real-world datasets demonstrate the superior performance of DEAE compared to the state-of-the-art methods based on both LMM and NLMM.