PAMAE adds a phase-aware router and expert mixture to flow-matching VLA models, yielding up to 9.2% higher task success on multi-stage manipulation simulations via two-stage training.
arXiv preprint arXiv:2507.10672 , year=
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GRASP maps natural language to bounding-box goals via VLM for neuro-symbolic planning and reports 73.3% success in 90 real-robot trials without task-specific training.
GaussianDream is a feed-forward 3D Gaussian world model plug-in that conditions VLA policies on learned 3D spatial and future evolution representations for improved robotic manipulation performance.
A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
AffordVLA improves VLA models for robotic manipulation by implicitly injecting affordance perception through feature alignment with a zero-shot teacher, claiming SOTA results in simulation and real-world tests.
STARRY uses unified diffusion to align spatial-temporal world predictions with action generation plus GASAM for geometry-aware attention, reaching 93.82%/93.30% success on 50 bimanual tasks in simulation and raising real-world success from 42.5% to 70.8%.
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.
A modular framework using FastSAM with Set-of-Mark prompting, an LLM as semantic router, and MoveIt Task Constructor achieves 62% end-to-end success in zero-shot language-guided robotic manipulation across open-world and spatial reasoning tasks.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.