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
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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