From Digital to Physical Reservoir Computing: Co-Optimizing Soft Robotic Reservoirs via Dynamics Matching

2026-08-01

AI Summary

This work addresses the performance gap between physical reservoir computing implemented on soft robots and its digital counterpart, which stems from suboptimal intrinsic dynamics. To bridge this gap, the authors propose a dynamics-matching co-optimization framework that jointly tunes the robot’s physical parameters, a diffeomorphic mapping between physical and reference states, and a feedforward–feedback controller to align the soft robot’s dynamics with those of a high-performing random oscillator network (RON) reference model. By leveraging a differentiable physics model, an acceleration-level equation error objective, and parallel multi-start gradient descent—thereby circumventing time integration—the approach achieves efficient dynamic alignment between physical and digital reservoirs. Evaluated on sMNIST and ADIAC classification tasks as well as Mackey–Glass and Lorenz96 forecasting benchmarks, the optimized system demonstrates an average performance improvement of 33.7% and closely approaches the digital reference model.

Abstract

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.

Problem

• Physical Reservoir Computing
• Soft Robotics
• Dynamics Matching
• Co-Optimization
• Temporal Memory

Innovation

• Physical Reservoir Computing
• Dynamics Matching
• Differentiable Modeling
• Soft Robotics
• Co-Optimization