Improving Generalization of Language-Conditioned Robot Manipulation

TL;DR

This paper introduces a two-stage framework for language-conditioned robot manipulation that learns from few demonstrations, improving generalization and enabling zero-shot transfer in real-world environments.

Contribution

The paper proposes an instance-level semantic fusion module and a two-stage task decomposition approach that enhances generalization in language-conditioned robot manipulation from limited data.

Findings

Improves generalization in unseen environments

Enables zero-shot manipulation in real robots

Performs well with few demonstrations

Abstract

The control of robots for manipulation tasks generally relies on visual input. Recent advances in vision-language models (VLMs) enable the use of natural language instructions to condition visual input and control robots in a wider range of environments. However, existing methods require a large amount of data to fine-tune VLMs for operating in unseen environments. In this paper, we present a framework that learns object-arrangement tasks from just a few demonstrations. We propose a two-stage framework that divides object-arrangement tasks into a target localization stage, for picking the object, and a region determination stage for placing the object. We present an instance-level semantic fusion module that aligns the instance-level image crops with the text embedding, enabling the model to identify the target objects defined by the natural language instructions. We validate our method on both simulation and real-world robotic environments. Our method, fine-tuned with a few demonstrations, improves generalization capability and demonstrates zero-shot ability in real-robot manipulation scenarios.