用程序化生成技术自动创建可让机器人操作的拼图，解决数据难产问题。
PhyRoGen: Synthetic Generation of Physical Robot Manipulation Puzzles Using Procedural Content Generation
- 基于程序化内容生成，自动生成带联动约束的物理拼图
- 生成24个可解拼图，300秒内均可被采样规划算法求解
- 适合作为机器人抓取算法评测和基础模型训练的数据集
机器人对物理拼图的操作对自动化装配与拆卸任务至关重要。然而，要使机器人学会解谜，需大量训练数据，而数据生成过程耗时且繁琐。为此，我们提出物理机器人操作拼图生成框架（PhyRoGen），利用程序化内容生成（PCG）技术自动构建合成的拼图数据集。PhyRoGen是通用拼图生成器，可生成具有物体间互锁依赖关系的拼图，即必须先操纵一个连杆物体，另一个才能移动。基于此框架，我们定义了六个具体生成器，生成24个物理拼图。通过基准测试框架，所有拼图均能在1至300秒内被采样规划算法求解。最后，我们在物理仿真中用KUKA LBR iiwa机器人验证了每个生成拼图的可操作性。结果表明，该框架能程序化生成独特且可解的机器人操作拼图，是评估操作算法和构建鲁棒基础模型的关键要素。
原文摘要 · Abstract (English)
Robot manipulation of physical puzzles is important for automatic assembly and disassembly tasks. However, to enable robots to solve physical puzzles, manipulation skills need to be learned, which requires large training datasets, the generation of which is often time consuming and tedious. To overcome this problem, we propose the Physical Robot Manipulation Puzzle Generation framework (PhyRoGen), which leverages procedural content generation (PCG) for automated generation of synthetic datasets of manipulation puzzles. PhyRoGen is a general-purpose puzzle generator, which can generate physical puzzles with interlocking object dependencies, where one articulated object must be manipulated before another can be moved. Based upon PhyRoGen, we define six concrete generators which we use to generate 24 physical puzzles. By using a benchmarking framework, we are able to solve all puzzles in 1 to 300 seconds using sampling-based planning algorithms. Finally, we demonstrate that every generated puzzle is manipulatable by using a KUKA LBR iiwa robot in a physical simulation. This shows that our framework is able to procedurally generate unique, solvable robot manipulation puzzles, which is a crucial ingredient to benchmark manipulation algorithms and to develop robust foundation models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。