EPIC-Bench is a perception‑centric benchmark for fine‑grained embodied visual grounding in vision‑language models. It contains 6.6k human‑annotated tuples of Image, Text, and Mask across the three core stages of embodied interaction—Target Localization, Navigation, and Manipulation. The benchmark offers 23 fine‑grained tasks, multi‑dimensional settings, and evaluates over 80 leading VLMs to provide diagnostic insights into their vision‑language grounding capabilities.
arXiv:2607. 09792v1 Announce Type: cross Abstract: Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness in open and uncertain real-world environments.
The paper introduces VGEBench, a new benchmark for evaluating Vision‑Language Models (VLMs) on generalizable, visually grounded exploration of household devices. Unlike existing datasets that rely on static images or annotated trajectories, VGEBench employs a logic‑driven state machine to simulate multi‑turn interaction loops, requiring agents to actively perceive, act, and refine their actions to achieve goals. Experiments show that current VLMs struggle to translate semantic knowledge into physical execution and to maintain long‑horizon state tracking.
arXiv:2604.18484v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from co...
GroundingPI is a 4‑billion‑parameter grounding foundation model that generates points and boxes as quantized coordinates in a shared vocabulary. It is trained with multimodal and spatial pretraining, supervised fine‑tuning, and reinforcement learning, achieving a new state‑of‑the‑art average of 73.68% across 34 grounding benchmarks. As a visual backbone, GroundingPI improves performance in robotic manipulation and autonomous driving, outperforming larger models and mainstream backbones in several out‑of‑distribution settings.
arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.
arXiv:2610.18836v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using the...
arXiv:2609.27076v1 Announce Type: new Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perce...
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and...
arXiv:2609.24526v2 Announce Type: replace Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints...
arXiv:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information...