Spatial Grounding for VLA Models

VLA: Vision-Language-Action

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37 papers10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement
Apr 12, 2026Zhaofeng Hu, Sifan Zhou, Qinbo Zhang +4Goal-Conditioned Robotic Manipulation Robotic Manipulation
Stony Brook University, Stony Brook, USA · Carnegie Mellon University, Pittsburgh, USA · MBZUAI, Abu Dhabi, UAE +1
ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models
Apr 10, 2026Nastaran Darabi, Amit Ranjan TrivediSpatial Grounding for VLA Models Vision-Language-Action Models
University of Illinois Chicago, IL, USA
SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation
Mar 24, 2026Ruisen Tu, Arth Shukla, Sohyun Yoo +5Spatial Grounding for VLA Models Vision-Language-Action Models
UC San Diego · Lambda, Inc.
VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models
Mar 23, 2026Zixuan Wang, Yuxin Chen, Yuqi Liu +6Vision-Language-Action Models Reasoning for VLA Models
HKUST · CUHK · SmartMore
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation
Mar 19, 2026Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah +3Visual Grounding for Robotics Spatial Grounding for VLA Models
Arizona State University · Haverford College
Look Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action Models
Mar 16, 2026Yulin Luo, Hao Chen, Zhuangzhe Wu +10Spatial Grounding for VLA Models Vision-Language-Action Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · The Chinese University of Hong Kong · Simplexity Robotics
Spatially Grounded Long-Horizon Task Planning in the Wild
Mar 13, 2026Sehun Jung, HyunJee Song, Dong-Hee Kim +4Robot Task Planning Long-Horizon Robotic Manipulation
Korea University · Microsoft Research · University of Wisconsin-Madison

Apr 12, 2026J/K move · Enter open · S save

AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement

Zhaofeng Hu, Sifan Zhou, Qinbo Zhang, Rongtao Xu+3

Stony Brook University, Stony Brook, USA · Carnegie Mellon University, Pittsburgh, USA · MBZUAI, Abu Dhabi, UAE+1

Goal-Conditioned Robotic Manipulation Robotic Manipulation Language-Conditioned Robot Manipulation Zero-Shot Robotic Manipulation Vision-Language-Action Models Spatial Grounding for VLA Models+1

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Vision-Language-Action (VLA) policies have emerged as a versatile paradigm for generalist robotic manipulation. However, precise object placement under compositional language remains challenging for end-to-end VLA policies. Slot-level placement requires reliable slot grounding and centimeter-level geometric precision. To this end, we propose AnySlot, a framework that reduces compositional complexity by introducing an explicit spatial visual goal between language grounding and control. AnySlot converts language into a visual goal by rendering a spatial marker at the intended slot, then executes this goal with a goal-conditioned VLA policy. This hierarchical design decouples high-level slot selection from low-level execution, improving semantic accuracy and spatial robustness. Furthermore, recognizing the lack of benchmarks for such precision-demanding tasks, we introduce SlotBench, a structured simulation benchmark with nine task categories for evaluating spatial reasoning in slot-level placement. Extensive experiments show that AnySlot significantly outperforms flat VLA baselines and modular grounding methods in zero-shot slot-level placement.