デジタルから物理へのリザーバーコンピューティング：ダイナミクスマッチングによるソフトロボットリザーバーの共最適化
From Digital to Physical Reservoir Computing: Co-Optimizing Soft Robotic Reservoirs via Dynamics Matching
ソフトロボットの物理リザーバーを、高性能なデジタルリザーバーのダイナミクスに合わせて事前学習・共最適化する手法を提案し、シミュレーションで分類・予測タスクの性能向上を実証した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Nicola Visentin, Maximilian Stölzle, Mariano Ramírez Montero, Francesco Braghin, Daniela Rus, Cosimo Della Santina
分類: cs.RO, cs.LG
原文アブストラクト
Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transformations, and efficient inference. However, physical reservoirs are often adopted as-is rather than pretrained or co-optimized, potentially limiting soft robotic PRC performance relative to digital reservoirs. We investigate whether a physical reservoir can instead be pretrained against high-performing digital reference dynamics. Our formulation jointly optimizes physical parameters, a diffeomorphic physical-reference state map, and feedforward-feedback control using a differentiable physical model and an acceleration-level equation-error objective that avoids temporal integration. As a proof of concept, we instantiate the formulation with simulated soft robots, a Random Oscillators Network (RON) reference, and parallel multi-start gradient descent. We evaluate the optimized reservoirs on classification (sMNIST and ADIAC) and forecasting (Mackey-Glass and Lorenz96) tasks across four reservoir dimensions. Compared with unoptimized soft robot reservoirs, the optimized reservoirs achieve a mean relative improvement of 33.7% across all tasks and datasets, while remaining close to the digital reference. These results demonstrate the feasibility of dynamics-level co-optimization for the simulated soft robotic reservoirs considered here.
