  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.
