PRISM：通过基于图像的场景和运动合成生成个性化机器人数据集
PRISM: Personalized Robotic Dataset Generation via Image-based Scene and Motion Synthesis
AI总结：
针对大规模预训练模型在特定环境泛化有限的问题，提出PRISM端到端管道，从单张图像和自然语言指令生成个性化机器人数据集，构建场景并合成演示，实验表明其效果良好。
摘要：
大规模预训练视觉语言动作模型推动了机器人策略学习，但在特定环境部署仍具挑战。遥操作成本高且难扩展，模拟虽易但难似目标环境。为此提出PRISM，可从单图像和自然语言指令生成个性化机器人数据集，构建场景并合成演示，实验验证了其有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
