Improving Generalization of Language-Conditioned Robot Manipulation
This addresses the challenge of data efficiency for robot manipulation in diverse settings, though it is incremental as it builds on existing vision-language models.
The paper tackles the problem of generalizing language-conditioned robot manipulation to unseen environments with minimal data by proposing a two-stage framework for object-arrangement tasks, achieving improved generalization and zero-shot ability in real-robot scenarios.
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.