We present Dynamics-Guided Diffusion Model, a data-driven framework for generating manipulator geometry designs for a given manipulation task. Instead of training different design models for each task, our approach employs a learned dynamics network shared across tasks. For a new manipulation task, we first decompose it into a collection of individual motion targets which we call target interaction profile, where each individual motion can be modeled by the shared dynamics network. The design objective constructed from the target and predicted interaction profiles provides a gradient to guide the refinement of finger geometry for the task. This refinement process is executed as a classifier-guided diffusion process, where the design objective acts as the classifier guidance. We evaluate our framework on various manipulation tasks, under the sensor-less setting using only an open-loop parallel jaw motion. Our generated designs outperform optimization-based and unguided diffusion baselines relatively by 31.5% and 45.3% on average manipulation success rate. With the ability to generate a design within 0.8 seconds, our framework could facilitate rapid design iteration and enhance the adoption of data-driven approaches for robotic mechanism design.

翻译：我们提出了动力学引导的扩散模型，这是一个数据驱动的框架，用于为给定操作任务生成操作器几何结构设计。不同于为每个任务训练不同的设计模型，我们的方法采用一个跨任务共享的动力学网络。对于新的操作任务，我们首先将其分解为一系列独立的运动目标（称为目标交互轮廓），其中每个独立运动可通过共享动力学网络建模。由目标交互轮廓与预测交互轮廓构建的设计目标，为任务的手指几何结构优化提供梯度引导。这一优化过程被实现为分类器引导的扩散过程，其中设计目标充当分类器指导。我们在无传感器设置下仅使用开环平行夹爪运动，对各种操作任务评估了该框架。生成的设计在平均操作成功率上相对基于优化的基线和无引导扩散基线分别提升了31.5%和45.3%。凭借在0.8秒内生成设计的能力，我们的框架可促进快速设计迭代，并推动数据驱动方法在机器人机构设计中的应用。