{"ID":2837931,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18322","arxiv_id":"2511.18322","title":"Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video","abstract":"Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require prior knowledge and manual design. We bridge this gap by introducing: (1) The Attention Broadcast Decoder (ABCD), a plug-and-play module for autoencoder-based latent dynamics learning that generates pixel-accurate attention maps localizing each latent dimension's contribution while filtering static backgrounds, enabling visual interpretability via spatially grounded latents and on-image overlays. (2) Visual Oscillator Networks (VONs), a 2D latent oscillator network coupled to ABCD attention maps for on-image visualization of learned masses, coupling stiffness, and forces, enabling mechanical interpretability. We validate our approach on single- and double-segment SCRs, demonstrating that ABCD-based models significantly improve multi-step prediction accuracy with 5.8x error reduction for Koopman operators and 3.5x for oscillator networks on a two-segment robot. VONs autonomously discover a chain structure of oscillators. This fully data-driven approach yields compact, mechanically interpretable models with potential relevance for future control applications.","short_abstract":"Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require prior knowledge and manual design. We bridge this gap by introducing: (1) The Attention Broadcast Decoder (ABCD), a plug-and-play module for...","url_abs":"https://arxiv.org/abs/2511.18322","url_pdf":"https://arxiv.org/pdf/2511.18322v3","authors":"[\"Henrik Krauss\",\"Johann Licher\",\"Naoya Takeishi\",\"Annika Raatz\",\"Takehisa Yairi\"]","published":"2025-11-23T07:27:39Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}