# A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter

## TL;DR

This paper introduces a joint vision-language-action model for target-oriented robotic grasping in clutter, enabling flexible instructions, improved efficiency, and better generalization without relying on object labels or extensive training data.

## Contribution

It presents a novel object-centric joint modeling approach that integrates vision, language, and action, enhancing flexibility and transferability in robotic grasping tasks.

## Key findings

- Achieves higher success rates with fewer motions in simulation and real-world tests.
- Generalizes effectively to unseen objects and instructions.
- Reduces reliance on object labels and handcrafted rules.

## Abstract

We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately conduct visual grounding to localize the target object, and generate a grasp for that object. However, these works require object labels or visual attributes for grounding, which calls for handcrafted rules in planner and restricts the range of language instructions. In this paper, we propose to jointly model vision, language and action with object-centric representation. Our method is applicable under more flexible language instructions, and not limited by visual grounding error. Besides, by utilizing the powerful priors from the pre-trained multi-modal model and grasp model, sample efficiency is effectively improved and the sim2real problem is relived without additional data for transfer. A series of experiments carried out in simulation and real world indicate that our method can achieve better task success rate by less times of motion under more flexible language instructions. Moreover, our method is capable of generalizing better to scenarios with unseen objects and language instructions. Our code is available at https://github.com/xukechun/Vision-Language-Grasping