GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal Conditioned Policy
<|reference_start|>GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal Conditioned Policy: The robotics community has consistently aimed to achieve generalizable robot manipulation with flexible natural language instructions. One of the primary challenges is that obtaining robot data fully annotated with both actions and texts is time-consuming and labor-intensive. However, partially annotated data, such as human activity videos without action labels and robot play data without language labels, is much easier to collect. Can we leverage these data to enhance the generalization capability of robots? In this paper, we propose GR-MG, a novel method which supports conditioning on both a language instruction and a goal image. During training, GR-MG samples goal images from trajectories and conditions on both the text and the goal image or solely on the image when text is unavailable. During inference, where only the text is provided, GR-MG generates the goal image via a diffusion-based image-editing model and condition on both the text and the generated image. This approach enables GR-MG to leverage large amounts of partially annotated data while still using language to flexibly specify tasks. To generate accurate goal images, we propose a novel progress-guided goal image generation model which injects task progress information into the generation process, significantly improving the fidelity and the performance. In simulation experiments, GR-MG improves the average number of tasks completed in a row of 5 from 3.35 to 4.04. In real-robot experiments, GR-MG is able to perform 47 different tasks and improves the success rate from 62.5% to 75.0% and 42.4% to 57.6% in simple and generalization settings, respectively. Code and checkpoints will be available at the project page: https://gr-mg.github.io/.<|reference_end|>
@article{li2024gr-mg:,
    title={GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal
  Conditioned Policy},
    author={Peiyan Li, Hongtao Wu, Yan Huang, Chilam Cheang, Liang Wang, Tao Kong},
    journal={arXiv preprint arXiv:2408.14368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14368},
    primaryClass={cs.RO cs.AI}
}