Dissipativity is a fundamental system-theoretic property closely related to stability, passivity, and input--output stability, and is particularly important in robotics, where learned dynamics models are often embedded within feedback control loops. However, most existing approaches for learning dissipative dynamics ar...
A deep discrete-time dissipative recurrent neural network (DissipNet) that explicitly enforces dissipativity, a key property related to stability and energy dissipation, through structural weight constraints and a dedicated training algorithm is proposed.
EquiGQNet is proposed, an efficient 6-DoF grasp quality evaluator that combines the strengths of both approaches to achieve grasping performance comparable to the early-fusion baseline and substantially outperforms late fusion on objects with complex geometry and limited graspable regions.
Sungwon Seo, Jaeseog Won, Ji-You Shin et al.· 0 citations
Parental absence can alter the emotional, behavioural, academic, and social environments in which adolescents develop, yet its consequences are not uniformly negative. This study examines psychological adjustment among 560 high school students living without one or both parents and identifies potential protective respo...
T. Luong, Ha Anh Thu Pham· EPRA International Journal o...· 0 citations
Across box-flipping and line-pressing tasks, URF achieves higher task success rates while reducing failure modes observed with admittance-only execution, including rapid force buildup, large force oscillations, tool breakage, and robot safety stops.
Ji-You Shin, Youngjin Seo, Jaeseog Won et al.· arXiv.org· 1 citation
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