A scholarly review underpins the field, citing a foundational reference on designer DNA-based machines with authors, date, and DOI.

The authors envision a future where robots are biological, programmable, and intelligent, enabling mastery of the molecular world.

DNA-based robots are being developed as nanoscale machines designed to operate inside living systems and manufacturing settings, with the potential to deliver targeted therapies and assemble nano-devices.

Researchers are exploring design strategies for stability and motion, including rigid joints, flexible components, and origami-inspired folding, to create controllable molecular machines.

Potential medical applications include nano-surgeons that identify and deliver therapies to specific cells and the possibility of capturing viruses such as SARS-CoV-2.

Additional challenges highlight limited robot functionality and a lack of infrastructure, such as DNA mechanical-property databases and reliable simulations.

Advancement will require cross-disciplinary collaboration, standardized DNA parts libraries, AI-assisted design simulations, and improved bio-manufacturing methods to scale DNA robots.

Major challenges include Brownian motion causing unpredictability, scaling from macro to molecular systems, and current designs that are static or limited in functionality, with insufficient databases on DNA mechanical properties and underdeveloped simulation tools.

Future directions advocate interdisciplinary innovation, including standardized DNA parts libraries, AI-enabled dynamic simulations, and advanced bio-manufacturing to scale DNA robots for real-world use.

Control of DNA robots relies on biochemical methods like DNA strand displacement combined with physical stimuli such as electric fields, magnetic fields, and light to achieve predictable motion.

Control methods blend chemistry and physics, encoding actions via DNA strand displacement while using external triggers to drive movement and shape change.

DNA strand displacement enables precise programming using fuel and structural strands, allowing high-accuracy control of robotic behavior.