Mobile robots combine sensory information with mechanical
actuation
to move autonomously through structured environments and perform specific
tasks. The miniaturization of such robots to the size of living cells
is actively pursued for applications in biomedicine, materials science,
and environmental sustainability. Existing microrobots based on field-driven
particles rely on knowledge of the particle position and the target
destination to control particle motion through fluid environments.
Often, however, these external control strategies are challenged by
limited information and global actuation where a common field directs
multiple robots with unknown positions. In this Perspective, we discuss
how time-varying magnetic fields can be used to encode the self-guided
behaviors of magnetic particles conditioned on local environmental
cues. Programming these behaviors is framed as a design problem: we
seek to identify the design variables (e.g., particle shape, magnetization,
elasticity, stimuli-response) that achieve the desired performance
in a given environment. We discuss strategies for accelerating the
design process using automated experiments, computational models,
statistical inference, and machine learning approaches. Based on the
current understanding of field-driven particle dynamics and existing
capabilities for particle fabrication and actuation, we argue that
self-guided microrobots with potentially transformative capabilities
are close at hand.