# ConPoSe: LLM-Guided Contact Point Selection for Scalable Cooperative Object Pushing

arXiv: https://arxiv.org/abs/2510.08705 · HTML: https://mlsanity.com/paper/2510.08705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs](https://mlsanity.com/paper/2402.07872/) (2024)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models](https://mlsanity.com/paper/2404.10399/) (2024)
- [Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration](https://mlsanity.com/paper/2412.18292/) (2024)
- [Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing](https://mlsanity.com/paper/2411.07104/) (2024)
- [ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models](https://mlsanity.com/paper/2404.02318/) (2024)
- [CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications](https://mlsanity.com/paper/2405.01107/) (2024)
- [Unwieldy Object Delivery with Nonholonomic Mobile Base: A Stable Pushing Approach](https://mlsanity.com/paper/2309.14295/) (2023)
- [Collaborative Planar Pushing of Polytopic Objects with Multiple Robots in Complex Scenes](https://mlsanity.com/paper/2405.07908/) (2024)
- [CollaBot: Vision-Language Guided Simultaneous Collaborative Manipulation](https://mlsanity.com/paper/2508.03526/) (2025)

## Abstract (arXiv)

Object transportation in cluttered environments is a fundamental task in various domains, including domestic service and warehouse logistics. In cooperative object transport, multiple robots must coordinate to move objects that are too large for a single robot. One transport strategy is pushing, which only requires simple robots. However, careful selection of robot-object contact points is necessary to push the object along a preplanned path. Although this selection can be solved analytically, the solution space grows combinatorially with the number of robots and object size, limiting scalability. Inspired by how humans rely on common-sense reasoning for cooperative transport, we propose combining the reasoning capabilities of Large Language Models with local search to select suitable contact points. Our LLM-guided local search method for contact point selection, ConPoSe, successfully selects contact points for a variety of shapes, including cuboids, cylinders, and T-shapes. We demonstrate that ConPoSe scales better with the number of robots and object size than the analytical approach, and also outperforms pure LLM-based selection.