Robot learning, particularly through vision–language–action (VLA) models, has rapidly improved the ability of robots to execute instructed tasks. However, the current mainstream paradigm of robot learning largely depends on one‐way instruction from humans to robots, lacking a cyclical structure in which knowledge acqui...
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
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 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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