Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara

TL;DR
This paper introduces a Cutting Sequence Diffuser (CSD) that enables efficient, sim-to-real transferable planning for robotic object shaping by grinding, using simulation data and diffusion models to generate long-horizon action sequences.
Contribution
The paper presents a novel CSD method that reduces the reality gap and planning time in robotic grinding tasks by leveraging simulation data and diffusion models for long-horizon planning.
Findings
Effective transfer from simulation to real robot grinding tasks.
Successful shaping of various target shapes across different materials.
Short planning time and high accuracy in shape attainment.
Abstract
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…
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Taxonomy
TopicsManufacturing Process and Optimization · Advanced Numerical Analysis Techniques · Image Processing and 3D Reconstruction
