Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video

• URL: http://arxiv.org/abs/2511.18322v1
• Date: Sun, 23 Nov 2025 07:27:39 GMT
• Title: Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
• Authors: Henrik Krauss, Johann Licher, Naoya Takeishi, Annika Raatz, Takehisa Yairi,
• Abstract summary: We introduce Attention Broadcast Decoder (ABCD), a plug-and-play module for autoencoder-based latent dynamics learning.<n>ABCD generates pixel-accurate attention maps localizing each latent dimension's contribution while filtering static backgrounds.<n>We validate our approach on single- and double-segment SCRs, demonstrating that ABCD-based models significantly improve multi-step prediction accuracy.
• Score: 4.857795247230421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven learning of soft continuum robot (SCR) dynamics from high-dimensional observations offers flexibility but often lacks physical interpretability, while model-based approaches require prior knowledge and can be computationally expensive. 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. (2) By coupling these attention maps to 2D oscillator networks, we enable direct on-image visualization of learned dynamics (masses, stiffness, and forces) without prior knowledge. We validate our approach on single- and double-segment SCRs, demonstrating that ABCD-based models significantly improve multi-step prediction accuracy: 5.7x error reduction for Koopman operators and 3.5x for oscillator networks on the two-segment robot. The learned oscillator network autonomously discovers a chain structure of oscillators. Unlike standard methods, ABCD models enable smooth latent space extrapolation beyond training data. This fully data-driven approach yields compact, physically interpretable models suitable for control applications.

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