用扩散模型规划磨削路径，低成本实现真实场景高效建模。
Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding
- 设计小步移除的平滑动作空间，降低形状变化复杂度。
- 仅需仿真数据训练，实现在多种材料上快速精准成型。
- 适合需要高精度、低耗时磨削规划的工业自动化场景。
机器人磨削自动成型是重要工业流程，涉及旋转磨带去除材料。由于材料类型、去除量和机械姿态等因素导致去除阻力复杂，难以建立精确分析模型。基于真实数据的数据驱动方法因采集成本高且过程不可逆而困难。本文提出切割序列扩散器（CSD），仅需简单仿真数据即可学习，能生成可迁移到现实世界的长时序动作序列。通过约束每步小体积去除，构建平滑动作空间以抑制去除阻力带来的形状演化复杂性，从而缩小仿真与现实差距。利用扩散模型生成长序列动作，显著减少规划时间，并确保每步符合小去除量约束。在仿真与真实机器人实验中验证，CSD可快速适应多种材料和目标形状，实现高效磨削。
原文摘要 · Abstract (English)
Automating object shaping by grinding with a robot is a crucial industrial process that involves removing material with a rotating grinding belt. This process generates removal resistance depending on such process conditions as material type, removal volume, and robot grinding posture, all of which complicate the analytical modeling of shape transitions. Additionally, a data-driven approach based on real-world data is challenging due to high data collection costs and the irreversible nature of the process. This paper proposes a Cutting Sequence Diffuser (CSD) for object shaping by grinding. The CSD, which only requires simple simulation data for model learning, offers an efficient way to plan long-horizon action sequences transferable to the real world. Our method designs a smooth action space with constrained small removal volumes to suppress the complexity of the shape transitions caused by removal resistance, thus reducing the reality gap in simulations. Moreover, by using a diffusion model to generate long-horizon action sequences, our approach reduces the planning time and allows for grinding the target shape while adhering to the constraints of a small removal volume per step. Through evaluations in both simulation and real robot experiments, we confirmed that our CSD was effective for grinding to different materials and various target shapes in a short time.
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