Embodied Intelligence for Advanced Bioinspired Microrobotics: Examples and Insights

TL;DR

This paper advocates for embodied intelligence (EI) as a design principle in microrobotics, emphasizing co-design of structure and behavior to achieve emergent intelligent locomotion and navigation, demonstrated through various bioinspired robots.

Contribution

It introduces the concept of co-design as a means to embed EI in microrobots, contrasting it with traditional architectures and showcasing several bioinspired robotic platforms.

Findings

Robots exhibit intelligent behavior emerging from structural dynamics.

Embedded feedback and sensing enable scalable, robust control.

Co-design enhances microrobotics beyond classical control methods.

Abstract

The term embodied intelligence (EI) conveys the notion that body morphology, material properties, interaction with the environment, and control strategies can be purposefully integrated into the process of robotic design to generate intelligent behavior; in particular, locomotion and navigation. In this paper, we discuss EI as a design principle for advanced microrobotics, with a particular focus on co-design -- the simultaneous and interdependent development of physical structure and behavioral function. To illustrate the contrast between EI-inspired systems and traditional architectures that decouple sensing, computation, and actuation, we present and discuss a collection of robots developed by the author and his team at the Autonomous Microrobotic Systems Laboratory (AMSL). These robots exhibit intelligent behavior that emerges from their structural dynamics and the physical interaction between their components and with the environment. Platforms such as the Bee++, RoBeetle, SMALLBug, SMARTI, WaterStrider, VLEIBot+, and FRISSHBot exemplify how feedback loops, decision logics, sensing mechanisms, and smart actuation strategies can be embedded into the physical properties of the robotic system itself. Along these lines, we contend that co-design is not only a method for empirical optimization under constraints, but also an enabler of EI, offering a scalable and robust alternative to classical control for robotics at the mm-to-cm-scale.