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
Imitation learning (IL) enables robots to acquire autonomous skills from human demonstrations. Recent in-context IL (ICIL) methods leverage off-the-shelf large language models (LLMs) as instant policies that infer task context and generate executable trajectories from only a few demonstrations, without retraining model...
Hanbit Oh, Andrea M. Salcedo-Vázquez, I. Ramirez-Alpizar et al.· IEEE Access· 0 citations
Robot manipulation policies trained through imitation learning inherit not only the demonstrated behavior but also the conservative execution tempo of robot demonstrations. Existing acceleration approaches can execute faster than the original demonstrations, but determine the appropriate acceleration primarily from rob...
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
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
