# AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation

arXiv: https://arxiv.org/abs/2506.12374 · HTML: https://mlsanity.com/paper/2506.12374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs](https://mlsanity.com/paper/2402.07872/) (2024)
- [HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation](https://mlsanity.com/paper/2502.05485/) (2025)
- [RoboVQA: Multimodal Long-Horizon Reasoning for Robotics](https://mlsanity.com/paper/2311.00899/) (2023)
- [SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2311.15707/) (2023)
- [SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images](https://mlsanity.com/paper/2501.04689/) (2025)
- [VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation](https://mlsanity.com/paper/2407.09829/) (2024)
- [Scalable Real2Sim: Physics-Aware Asset Generation Via Robotic Pick-and-Place Setups](https://mlsanity.com/paper/2503.00370/) (2025)
- [Vision-Language Model Predictive Control for Manipulation Planning and Trajectory Generation](https://mlsanity.com/paper/2504.05225/) (2025)

## Abstract (arXiv)

Natural-language instructions specify manipulation goals but leave the robot's motion underdetermined. We present AntiGrounding, a visual action-selection framework built around a dual geometric--visual trajectory interface. Each short trajectory retained after feasibility filtering remains an explicit motion plan and serves as a visual prompt for instruction-conditioned vision--language model (VLM) assessment. Structured multi-view visual question answering (VQA) scores safety, task alignment, efficiency, and physical plausibility. Weighted view fusion aggregates these scores for trajectory selection, while the scores also guide subsequent translational proposals. Separate orientation and gripper controls coordinate physical interaction. Planning proceeds in an initialized digital twin, which validates selected segments before the real robot executes the same waypoint sequences. Across eight real-world manipulation tasks, AntiGrounding with a single GPT-6 Astra evaluator achieves AstraOverall% overall success. Under the reported deployment protocol, π0.5 achieves PiOverall%, and a PIVOT-style visual proposal-selection baseline with the same evaluator achieves PivotOverall%. Component ablations and evaluator sensitivity characterize trajectory assessment, proposal search, orientation control, and evaluator choice. Performance depends on digital-twin fidelity and physical interaction.