Wireless bioelectronic control architectures for biohybrid robotic systems

TL;DR

This paper presents a system-level framework for wireless bioelectronic control of biohybrid robots, analyzing control strategies, design trade-offs, and proposing pathways toward autonomous systems.

Contribution

It introduces a unifying engineering framework linking device design to system control, emphasizing tissue-device interface constraints and design principles for biohybrid robotics.

Findings

Analyzes three control strategies: electrical, optoelectronic, and neuromuscular.

Identifies tissue-device interface as a key constraint affecting system performance.

Proposes a transition from open-loop to closed-loop biohybrid control systems.

Abstract

Wireless bioelectronic interfaces are increasingly used to control tissue-engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system-level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co-design problem of integrating signal delivery, spatial selectivity, scalability, and interface stability. We analyze three representative control strategies, wireless electrical stimulation, wireless optoelectronic stimulation, and neuromuscular integration, which operates within a distinct regime with characteristic trade-offs. Across these modalities, the tissue-device interface emerges as a key constraint, governing the interplay between electromagnetic coupling, circuit performance, and biomechanical response. Based on this framework, we outline practical design principles spanning electromagnetic field distribution, circuit architecture, and actuator mechanics. We further propose a transition from open-loop stimulation to closed-loop biohybrid autonomy enabled by organoid-integrated bioelectronics and bidirectional microelectrode interfaces. This work establishes a system-level perspective on wireless bioelectronic control and provides design guidelines for developing stable, scalable, and autonomous biohybrid robotic systems.