Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning

Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping across unseen views. In response, we introduce GenSplat, a feed-forward 3D Gaussian Splatting framework that facilitates view-generalized policy learning through novel view rendering. GenSplat employs a permutation-equivariant architecture to reconstruct high-fidelity 3D scenes from sparse, uncalibrated inputs in a single forward pass. To ensure structural integrity, we design a 3D-prior distillation strategy that regularizes the 3DGS optimization, preventing the geometric collapse typical of purely photometric supervision. By rendering diverse synthetic views from these stable 3D representations, we systematically augment the observational manifold during training. This augmentation forces the policy to ground its decisions in underlying 3D structures, thereby ensuring robust execution under severe spatial perturbations where baselines severely degrade.

Scalable Air-to-Ground Wireless Channel Modeling Using Environmental Context and Generative Diffusion

The fast motion of Low Earth Orbit (LEO) satellites causes the propagation channel to vary rapidly, and its behavior is strongly shaped by the surrounding environment, especially at low elevation angles where signals are highly susceptible to terrain blockage and other environmental effects. Existing studies mostly rely on assumed statistical channel distributions and therefore ignore the influence of the actual geographic environment. In this paper, we propose an environment-aware channel modeling method for air-to-ground wireless links. We leverage real environmental data, including digital elevation models (DEMs) and land cover information, together with ray tracing (RT) to determine whether a link is line-of-sight (LOS) or non-line-of-sight (NLOS) and to identify possible reflection paths of the signal. The resulting obstruction and reflection profiles are then combined with models of diffraction loss, vegetation absorption, and atmospheric attenuation to quantitatively characterize channel behavior in realistic geographic environments. Since RT is computationally intensive, we use RT-generated samples and environmental features to train a scalable diffusion model that can efficiently predict channel performance for arbitrary satellite and ground terminal positions, thereby supporting real-time decision-making. In the experiments, we validate the proposed model with measurement data from both cellular and LEO satellite links, demonstrating its effectiveness in realistic environments.

SAMPO:Scale-wise Autoregression with Motion PrOmpt for generative world models

World models allow agents to simulate the consequences of actions in imagined environments for planning, control, and long-horizon decision-making. However, existing autoregressive world models struggle with visually coherent predictions due to disrupted spatial structure, inefficient decoding, and inadequate motion modeling. In response, we propose \textbf{S}cale-wise \textbf{A}utoregression with \textbf{M}otion \textbf{P}r\textbf{O}mpt (\textbf{SAMPO}), a hybrid framework that combines visual autoregressive modeling for intra-frame generation with causal modeling for next-frame generation. Specifically, SAMPO integrates temporal causal decoding with bidirectional spatial attention, which preserves spatial locality and supports parallel decoding within each scale. This design significantly enhances both temporal consistency and rollout efficiency. To further improve dynamic scene understanding, we devise an asymmetric multi-scale tokenizer that preserves spatial details in observed frames and extracts compact dynamic representations for future frames, optimizing both memory usage and model performance. Additionally, we introduce a trajectory-aware motion prompt module that injects spatiotemporal cues about object and robot trajectories, focusing attention on dynamic regions and improving temporal consistency and physical realism. Extensive experiments show that SAMPO achieves competitive performance in action-conditioned video prediction and model-based control, improving generation quality with 4.4$\times$ faster inference. We also evaluate SAMPO's zero-shot generalization and scaling behavior, demonstrating its ability to generalize to unseen tasks and benefit from larger model sizes.