# Residual RL-MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow

arXiv: https://arxiv.org/abs/2603.05448 · HTML: https://mlsanity.com/paper/2603.05448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation](https://mlsanity.com/paper/2403.14414/) (2024)
- [Autonomous Vision-Based Magnetic Microrobotic Pushing of Micro-Objects and Cells](https://mlsanity.com/paper/2505.06450/) (2025)
- [Real-time autonomous magnetic microrobot navigation across dynamic and biologically relevant environments](https://mlsanity.com/paper/2509.05500/) (2025)

## Abstract (arXiv)

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 robot-cell contact, preserving reliable approach behavior and stabilizing learning. All methods share the same actuation interface and speed envelope for fair comparisons. Simulation results show improved robustness and tracking accuracy over pure MPC and PID under nonstationary flow, with generalization from a clover training curve to unseen circle and square trajectories. A residual-bound sweep identifies an intermediate correction limit as the best trade-off, which we use in all benchmarks.