AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making

• URL: http://arxiv.org/abs/2506.12374v2
• Date: Tue, 24 Jun 2025 10:01:18 GMT
• Title: AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making
• Authors: Wenbo Li, Shiyi Wang, Yiteng Chen, Huiping Zhuang, Qingyao Wu,
• Abstract summary: AntiGrounding is a new framework that reverses the instruction grounding process.<n>It lifts candidate actions directly into the VLM representation space.<n>It renders trajectories from multiple views, and uses structured visual question answering for instruction-based decision making.
• Score: 35.83717913117858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) encode knowledge and reasoning capabilities for robotic manipulation within high-dimensional representation spaces. However, current approaches often project them into compressed intermediate representations, discarding important task-specific information such as fine-grained spatial or semantic details. To address this, we propose AntiGrounding, a new framework that reverses the instruction grounding process. It lifts candidate actions directly into the VLM representation space, renders trajectories from multiple views, and uses structured visual question answering for instruction-based decision making. This enables zero-shot synthesis of optimal closed-loop robot trajectories for new tasks. We also propose an offline policy refinement module that leverages past experience to enhance long-term performance. Experiments in both simulation and real-world environments show that our method outperforms baselines across diverse robotic manipulation tasks.

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