CoFRIDA：人間とロボットの共同描画のための自己教師ありファインチューニング
CoFRIDA: Self-Supervised Fine-Tuning for Human-Robot Co-Painting
人間が描いたキャンバスにロボットが続きを描く共同描画フレームワークCoFRIDAを提案し、ロボットの制約を自己教師あり学習でテキスト画像モデルに組み込むことで、より正確な協調描画を実現した。
著者: Peter Schaldenbrand, Gaurav Parmar, Jun-Yan Zhu, James McCann, Jean Oh
分類: cs.RO
原文アブストラクト
Prior robot painting and drawing work, such as FRIDA, has focused on decreasing the sim-to-real gap and expanding input modalities for users, but the interaction with these systems generally exists only in the input stages. To support interactive, human-robot collaborative painting, we introduce the Collaborative FRIDA (CoFRIDA) robot painting framework, which can co-paint by modifying and engaging with content already painted by a human collaborator. To improve text-image alignment, FRIDA's major weakness, our system uses pre-trained text-to-image models; however, pre-trained models in the context of real-world co-painting do not perform well because they (1) do not understand the constraints and abilities of the robot and (2) cannot perform co-painting without making unrealistic edits to the canvas and overwriting content. We propose a self-supervised fine-tuning procedure that can tackle both issues, allowing the use of pre-trained state-of-the-art text-image alignment models with robots to enable co-painting in the physical world. Our open-source approach, CoFRIDA, creates paintings and drawings that match the input text prompt more clearly than FRIDA, both from a blank canvas and one with human created work. More generally, our fine-tuning procedure successfully encodes the robot's constraints and abilities into a foundation model, showcasing promising results as an effective method for reducing sim-to-real gaps.
