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
This work proposes a reinforcement learning approach that additionally leverages sliding and rolling contact modes and directly optimizes joint-space trajectories without analytical contact models or custom parameterizations, and minimize the robot-dynamics gap through minimum-jerk system identification and train uncer...
Abdullah Mustafa, Ryo Hanai, I. Ramirez-Alpizar 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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