RoboGround: Robotic Manipulation with Grounded Vision-Language Priors
This work addresses generalization in robotic manipulation for robotics applications, representing an incremental improvement through a novel method for a known bottleneck.
The paper tackles robotic manipulation by using grounding masks as intermediate representations to guide policy networks, resulting in significantly enhanced generalization abilities for robot policies.
Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding masks as an effective intermediate representation, balancing two key advantages: (1) effective spatial guidance that specifies target objects and placement areas while also conveying information about object shape and size, and (2) broad generalization potential driven by large-scale vision-language models pretrained on diverse grounding datasets. We introduce RoboGround, a grounding-aware robotic manipulation system that leverages grounding masks as an intermediate representation to guide policy networks in object manipulation tasks. To further explore and enhance generalization, we propose an automated pipeline for generating large-scale, simulated data with a diverse set of objects and instructions. Extensive experiments show the value of our dataset and the effectiveness of grounding masks as intermediate guidance, significantly enhancing the generalization abilities of robot policies.