ALDM-Grasping: 拡散モデルによるロボット把持のゼロショットSim-to-Real転移
ALDM-Grasping: Diffusion-aided Zero-Shot Sim-to-Real Transfer for Robot Grasping
拡散モデルでシミュレーション画像をフォトリアルに変換し、実環境とのギャップを埋めることで、ロボット把持タスクのゼロショットSim-to-Real転移を実現した研究。
著者: Yiwei Li, Zihao Wu, Huaqin Zhao, Tianze Yang, Zhengliang Liu, Peng Shu, Jin Sun, Ramviyas Parasuraman, Tianming Liu
分類: cs.RO
原文アブストラクト
To tackle the "reality gap" encountered in Sim-to-Real transfer, this study proposes a diffusion-based framework that minimizes inconsistencies in grasping actions between the simulation settings and realistic environments. The process begins by training an adversarial supervision layout-to-image diffusion model(ALDM). Then, leverage the ALDM approach to enhance the simulation environment, rendering it with photorealistic fidelity, thereby optimizing robotic grasp task training. Experimental results indicate this framework outperforms existing models in both success rates and adaptability to new environments through improvements in the accuracy and reliability of visual grasping actions under a variety of conditions. Specifically, it achieves a 75\% success rate in grasping tasks under plain backgrounds and maintains a 65\% success rate in more complex scenarios. This performance demonstrates this framework excels at generating controlled image content based on text descriptions, identifying object grasp points, and demonstrating zero-shot learning in complex, unseen scenarios.
