PRISM: 投影ベース報酬統合によるシーン認識型Real-to-Sim-to-Real転移
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations
少数の実演から画像で物体を認識し3Dモデルを取得してシミュレーション環境を自動構築し、VLM監督の投影ベース報酬でRL方策を訓練して実世界に転移する手法を提案。
詳しい要約
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著者: Haowen Sun, Han Wang, Chengzhong Ma, Shaolong Zhang, Jiawei Ye, Xingyu Chen, Xuguang Lan
分類: cs.RO, cs.AI
原文アブストラクト
Learning from few demonstrations to develop policies robust to variations in robot initial positions and object poses is a problem of significant practical interest in robotics. Compared to imitation learning, which often struggles to generalize from limited samples, reinforcement learning (RL) can autonomously explore to obtain robust behaviors. Training RL agents through direct interaction with the real world is often impractical and unsafe, while building simulation environments requires extensive manual effort, such as designing scenes and crafting task-specific reward functions. To address these challenges, we propose an integrated real-to-sim-to-real pipeline that constructs simulation environments based on expert demonstrations by identifying scene objects from images and retrieving their corresponding 3D models from existing libraries. We introduce a projection-based reward model for RL policy training that is supervised by a vision-language model (VLM) using human-guided object projection relationships as prompts, with the policy further fine-tuned using expert demonstrations. In general, our work focuses on the construction of simulation environments and RL-based policy training, ultimately enabling the deployment of reliable robotic control policies in real-world scenarios.
