Shape control of deformable linear objects (DLOs) is challenging for imitation learning because deformation behavior varies with material properties such as stiffness and elasticity, so a single policy must generate different action sequences for different objects even when the goal shape is identical. We propose a dif...
Ryunosuke Yamada, Tomohiro Motoda, Y. Domae et al.· 0 citations
Applying an imitation learning policy to a new manipulation task usually requires collecting new demonstrations and retraining the model, which makes sample efficiency a practical concern. Pretraining on large-scale robot datasets is effective in this respect, but such datasets are costly to collect and train on, while...
Shogo Iwakata, Tomohiro Motoda, Ryosuke Yamada et al.· 0 citations
Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with standard next-token objectives. FAST has substantially improved this representation by compactly encoding action containing diverse temporal frequencies into relatively few...
Keisuke Shirai, Tomohiro Motoda, Hanbit Oh et al.· 0 citations
High-precision insertion remains a fundamental challenge in robotic manipulation due to the strict alignment requirements and contact-rich interactions involved. Although peg-in-hole tasks are widely used for evaluation, existing bench- marks often rely on fixed task configurations, limiting their ability to assess rob...
Y. Delgado, José G. Buenaventura-Carreón, Floris Erich et al.· 0 citations
This work proposes Online Residual Policy Adaptation (ORPA), a framework that enables immediate, feedback-driven correction of robot actions without modifying the underlying policy parameters.
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai et al.· 1 citation
This survey examines what can be reused across robot embodiments and what must still be implemented on a new robot, and examines recent work through three overlapping research directions: sharing semantics and perception, sharing robot data and interfaces, and learning correspondence across embodiments.
Y. Domae, Keisuke Shirai, Hanbit Oh et al.· Trans. Mach. Learn. Res.· 0 citations
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