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
Grasping and holding tools while using them presents a considerable challenge not only for robots but also for humans. Such a challenge is particularly noticeable in processes involving assembly and disassembly, where efficiency and consistency depend on performing rapidly adaptive tasks. Nonetheless, contemporary robo...
R. Mykhailyshyn, Y. Domae, Kensuke Harada· IEEE Transactions on Automat...· 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
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