Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv:2606.27504v2 Announce Type: replace Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
arXiv:2607.10744v5 Announce Type: replace Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models...
arXiv:2604.13183v4 Announce Type: replace Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
The paper introduces the Unified Path Planner (UPP), a graph‑search algorithm that balances safety and optimality by adaptively weighting heuristics and using a local inverse‑distance safety field. UPP auto‑tunes its parameters during search, guaranteeing suboptimality bounds while improving obstacle clearance. Evaluation on ten simulated environments shows UPP achieving a 0.94 OptiSafe score—significantly higher than existing methods—while adding only 0.5–1% to path length and maintaining a 100% success rate, with hardware validation on a TurtleBot confirming practical benefits.
arXiv:2608.23140v1 Announce Type: new Abstract: Achieving 360{\deg} coverage is critical for the visual perception systems of autonomous vehicles. Fisheye cameras offer a cost-effective solution by e...
arXiv:2608.21444v1 Announce Type: new Abstract: Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue (SAR) and critical infrastructure monitoring. Ye...
arXiv:2608.22160v1 Announce Type: new Abstract: Physical automation is scaling toward fleets of embodied machines commanded by an AI brain. Early deployments already run factories and warehouses at p...
The paper introduces CloudEdgeVLA, a cloud‑edge policy for Vision‑Language‑Action models that treats temporal misalignment as a representation‑learning problem. It encodes delayed observations into slowly varying task features on the cloud while a lightweight edge head fuses the latest cloud feature with current local vision. Experiments on four LIBERO suites show that CloudEdgeVLA retains 63.8–78.0% success under a 40‑step delay window, far outperforming VLASH and single‑path baselines.
arXiv:2608.21776v1 Announce Type: new Abstract: Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes...
arXiv:2507.04356v3 Announce Type: replace-cross Abstract: Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This...
arXiv:2608.21926v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) navigation in modern low-altitude environments requires more accurate pose alignment in the final approach stage for targ...
arXiv:2608.23100v1 Announce Type: cross Abstract: Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological ev...
The paper introduces HOT-POT, a method that applies optimal transport to sparse stereo matching, addressing challenges such as occlusions, motion, and camera distortions. By modeling camera‑projected points as half‑lines and using epipolar and 3D ray distances as cost functions, the authors formulate efficient assignment problems for unsupervised sparse matching. The approach is extended to hierarchical optimal transport for unsupervised object matching, with experiments demonstrating its effectiveness in facial analysis for aligning different landmarking conventions.
ViSMoE introduces a visual‑aware sparse Mixture‑of‑Experts framework for embodied referring expression grounding, enabling an agent to navigate real environments and localize a target object from natural language instructions. By routing visual information through specialized experts, the method produces discriminative representations for both navigation views and candidate objects, unlike prior approaches that use a single vision encoder. Experiments on the REVERIE and SOON datasets show that ViSMoE surpasses existing state‑of‑the‑art methods.
arXiv:2608.21402v1 Announce Type: cross Abstract: World action models (WAMs) jointly denoise future video frames and robot actions, and the video prior is expected to generalize their control. Camera...
arXiv:2608.21407v1 Announce Type: cross Abstract: Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action...
The paper introduces a human‑centric video world model that can be controlled by tracked head pose and joint‑level hand poses, enabling realistic hand‑object interactions. It evaluates existing diffusion transformer conditioning methods and proposes a new 3‑D control mechanism, training a bidirectional diffusion model and distilling it into a causal, interactive system for egocentric virtual environments. Human‑subject experiments show that the system improves task performance and gives users a higher perceived sense of control compared to baseline methods.
arXiv:2608.22398v1 Announce Type: cross Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
Triplet2Track (TTS) is a closed‑loop long‑horizon imitation learning system that uses human videos to reduce robot‑collected data. It represents high‑level subgoals as instance‑grounded triplets, converts them into continuous track priors for execution, and monitors task progress from observations for online replanning. In diverse real‑world long‑horizon tasks, TTS achieves a 74.8% average success rate and supports object‑level and compositional generalization.
VeCAS is a two‑stage framework that generates contrast‑free X‑ray angiograms from non‑contrast images. Stage I localizes vascular structures using a discriminative model and cross‑modality latent distillation, while Stage II synthesizes angiographic appearance within those regions with a vessel‑focused inpainting model. Experiments on a lower‑limb vascular intervention dataset show that VeCAS improves vascular structural fidelity and image quality, and robotic guidewire navigation tests demonstrate significant reductions in time to target and operation steps.