We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and collision. As the number of objects increases, finding valid poses becomes more difficult, necessitating significant human engineering effort, which limits the diversity of the scenes. To overcome these challenges, we propose a reinforcement learning method that can be trained with physics-based reward signals provided by the simulator. Our experiments demonstrate that ClutterGen can generate cluttered object layouts with up to ten objects on confined table surfaces. Additionally, our policy design explicitly encourages the diversity of the generated scenes for open-ended generation. Our real-world robot results show that ClutterGen can be directly used for clutter rearrangement and stable placement policy training.

翻译：本文提出ClutterGen，一种符合物理规律的仿真场景生成器，能够为机器人学习生成高度多样化、杂乱且稳定的场景。生成此类场景具有挑战性，因为每个物体都必须遵循重力与碰撞等物理定律。随着物体数量的增加，寻找有效位姿的难度急剧上升，通常需要大量人工设计工作，这严重限制了场景的多样性。为克服这些挑战，我们提出一种强化学习方法，该方法可通过仿真器提供的基于物理的奖励信号进行训练。实验表明，ClutterGen能在受限桌面空间上生成包含多达十个物体的杂乱物体布局。此外，我们的策略设计明确鼓励生成场景的多样性，以实现开放式生成。真实机器人实验结果表明，ClutterGen可直接用于杂乱场景重排与稳定放置策略的训练。