PRISM: 画像ベースのシーン・動作合成によるパーソナライズドロボットデータセット生成
PRISM: Personalized Robotic Dataset Generation via Image-based Scene and Motion Synthesis
単一画像と自然言語指示から、ユーザ環境に適合した多様なデジタル双子シーンと実行可能なデモを自動生成し、ロボットポリシー学習用のパーソナライズドデータセットを構築するパイプラインを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Dogyu Ko, Haneul Kim, Chanyoung Yeo, Dowoon Lee, Taeho Park, Hyoseok Hwang
分類: cs.RO
原文アブストラクト
Recent advances in large-scale pretrained vision-language-action models have improved robot policy learning, but directly deploying such policies in user-specific environments remains challenging due to limited generalization, which inevitably requires collecting a dataset tailored to the target environment. Teleoperation yields well-aligned data but is costly and difficult to scale, whereas simulation scales easily but struggles to resemble the target environment and generate task-specific trajectories. To meet both simultaneously, we propose PRISM, an end-to-end pipeline that generates personalized robotic datasets from a single image and a natural-language instruction. PRISM constructs digital cousin scenes that are semantically and geometrically aligned with the user environment yet diverse at the instance level, and synthesizes executable demonstrations without human teleoperation. Extensive experiments show that policies trained on PRISM-generated datasets outperform those trained on baseline-generated datasets on LIBERO and LIBERO-Plus, achieve up to 100\% success rate on three real-world manipulation tasks, and maintain stronger performance when evaluated in environments that differ from those seen during training.
