arXiv:2603.05448v2 Announce Type: replace-cross Abstract: Contact-rich micromanipulation in microfluidic flow is challenging because small disturbances can break pushing contact and induce large lateral drift. We study planar cell pushing with a magnetic rolling microrobot that tracks a waypoint-sampled reference curve under time-varying Poiseuille flow in simulation. We propose a hybrid controller that augments a nominal MPC with a learned residual policy trained by SAC. The policy outputs a bounded 2D velocity correction that is contact-gated, so residual actions are applied only during robo
This research addresses fundamental challenges in precise micromanipulation under dynamic conditions, a critical hurdle for advanced microrobotics, suggesting a maturation of control techniques for highly complex micro-scale tasks.
The robust control of microrobots, especially in contact-rich and fluidic environments, is key to advancing capabilities in areas like targeted drug delivery, cellular manipulation, and micro-assembly, opening new avenues for medical and industrial applications.
The integration of AI (residual policies) with traditional control (MPC) enhances the adaptability and robustness of microrobots in unpredictable microfluidic settings, moving beyond simpler, pre-programmed movements.
- · Biotech and medical device companies
- · Microrobotics researchers and developers
- · Microfluidics industry
- · AI/Control systems integration firms
- · Developers of less adaptable micromanipulation systems
Improved reliability and precision in micro-scale manipulation tasks such as cell sorting or targeted therapy delivery.
Acceleration of research and development in drug discovery and personalized medicine due to enhanced cellular-level control.
Potential for new therapeutic interventions that rely on precisely positioned microscopic agents within the human body.
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Read at arXiv cs.AI