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 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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