arXiv subjects

Sambeeta Das

Publications and source records attributed to Sambeeta Das.

11 recordsLinked to original sources

arXiv2026

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

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.

cs.RO↗

arXiv2026

MicroPush: A Simulator and Benchmark for Contact-Rich Cell Pushing and Assembly with a Magnetic Rolling Microrobot

Magnetic rolling microrobots enable gentle manipulation in confined microfluidic environments, yet autonomy for contact-rich behaviors such as cell pushing and multi-target assembly remains difficult to develop and evaluate reproducibly. We present MicroPush, an open-source simulator and benchmark suite for magnetic rolling microrobots in cluttered 2D scenes. MicroPush combines an overdamped interaction model with contact-aware stick--slip effects, lightweight near-field damping, optional Poiseuille background flow, and a calibrated mapping from actuation frequency to free-space rolling speed. On top of the simulator core, we provide a modular planning--control stack with a two-phase strategy for contact establishment and goal-directed pushing, together with a deterministic benchmark protocol with fixed tasks, staged execution, and unified CSV logging for single-object transport and hexagonal assembly. We report success, time, and tracking metrics, and an actuation-variation measure $E_{\Delta\omega}$. Results show that controller stability dominates performance under flow disturbances, while planner choice can influence command smoothness over long-horizon sequences via waypoint progression. MicroPush enables reproducible comparison and ablation of planning, control, and learning methods for microscale contact-rich micromanipulation.

cs.RO↗

arXiv2025

MicroRoboScope: A Portable and Integrated Mechatronic Platform for Magnetic and Acoustic Microrobotic Experimentation

This paper presents MicroRoboScope, a portable, compact, and versatile microrobotic experimentation platform designed for real-time, closed-loop control of both magnetic and acoustic microrobots. The system integrates an embedded computer, microscope, power supplies, and control circuitry into a single, low-cost and fully integrated apparatus. Custom control software developed in Python and Arduino C++ handles live video acquisition, microrobot tracking, and generation of control signals for electromagnetic coils and acoustic transducers. The platform's multi-modal actuation, accessibility, and portability make it suitable not only for specialized research laboratories but also for educational and outreach settings. By lowering the barrier to entry for microrobotic experimentation, this system enables new opportunities for research, education, and translational applications in biomedicine, tissue engineering, and robotics.

cs.RO↗

arXiv2025

Microrobot Vascular Parkour: Analytic Geometry-based Path Planning with Real-time Dynamic Obstacle Avoidance

Autonomous microrobots in blood vessels could enable minimally invasive therapies, but navigation is challenged by dense, moving obstacles. We propose a real-time path planning framework that couples an analytic geometry global planner (AGP) with two reactive local escape controllers, one based on rules and one based on reinforcement learning, to handle sudden moving obstacles. Using real-time imaging, the system estimates the positions of the microrobot, obstacles, and targets and computes collision-free motions. In simulation, AGP yields shorter paths and faster planning than weighted A* (WA*), particle swarm optimization (PSO), and rapidly exploring random trees (RRT), while maintaining feasibility and determinism. We extend AGP from 2D to 3D without loss of speed. In both simulations and experiments, the combined global planner and local controllers reliably avoid moving obstacles and reach targets. The average planning time is 40 ms per frame, compatible with 25 fps image acquisition and real-time closed-loop control. These results advance autonomous microrobot navigation and targeted drug delivery in vascular environments.

cs.RO↗

arXiv2025

Autonomous Vision-Based Magnetic Microrobotic Pushing of Micro-Objects and Cells

Accurate and autonomous transportation of micro-objects and biological cells can enable significant advances in a wide variety of research disciplines. Here, we present a novel, vision-based, model-free microrobotic pushing algorithm for the autonomous manipulation of micro objects and biological cells. The algorithm adjusts the axis of a rotating magnetic field that in turn controls the heading angle and spin axis of a spherical Janus rolling microrobot. We introduce the concept of a microrobotic guiding corridor to constrain the object and to avoid pushing failures. We then show that employing only two simple conditions, the microrobot is able to successfully and autonomously push microscale objects along predefined trajectories. We evaluate the performance of the algorithm by measuring the mean absolute error and completion time relative to a desired path at different actuation frequencies and guiding corridor widths. Finally, we demonstrate biomedical applicability by autonomously transporting a single biological cell, highlighting the methods potential for applications in tissue engineering, drug delivery and synthetic biology.

cs.RO↗

arXiv2024

Model Predictive Control for Magnetically-Actuated Cellbots

This paper presents a control framework for magnetically actuated cellbots, which combines Model Predictive Control (MPC) with Gaussian Processes (GPs) as a disturbance estimator for precise trajectory tracking. To address the challenges posed by unmodeled dynamics, we integrate data-driven modeling with model-based control to accurately track desired trajectories using relatively small data. To the best of our knowledge, this is the first work to integrate data-driven modeling with model-based control for the magnetic actuation of cellbots. The GP effectively learns and predicts unmodeled disturbances, providing uncertainty bounds as well. We validate our method through experiments with cellbots, demonstrating improved trajectory tracking accuracy.

cs.RO↗

arXiv2022

Learning a Tracking Controller for Rolling $\mu$bots

Micron-scale robots ($\mu$bots) have recently shown great promise for emerging medical applications. Accurate controlling $\mu$bots, while critical to their successful deployment, is challenging. In this work, we consider the problem of tracking a reference trajectory using a $\mu$bot in the presence of disturbances and uncertainty. The disturbances primarily come from Brownian motion and other environmental phenomena, while the uncertainty originates from errors in the model parameters. We model the $\mu$bot as an uncertain unicycle that is controlled by a global magnetic field. To compensate for disturbances and uncertainties, we develop a nonlinear mismatch controller. We define the model mismatch error as the difference between our model's predicted velocity and the actual velocity of the $\mu$bot. We employ a Gaussian Process to learn the model mismatch error as a function of the applied control input. Then we use a least-squares minimization to select a control action that minimizes the difference between the actual velocity of the $\mu$bot and a reference velocity. We demonstrate the online performance of our joint learning and control algorithm in simulation, where our approach accurately learns the model mismatch and improves tracking performance. We also validate our approach in an experiment and show that certain error metrics are reduced by up to $40\%$.

cs.RO↗

arXiv2022

Closed-loop Control of Catalytic Janus Microrobots

We report a closed-loop control system for paramagnetic catalytically self-propelled Janus microrobots. We achieve this control by employing electromagnetic coils that direct the magnetic field in a desired orientation to steer the microrobots. The microrobots move due to the catalytic decomposition of hydrogen peroxide, during which they align themselves to the magnetic torques applied to them. Because the angle between their direction of motion and their magnetic orientation is a priori unknown, an algorithm is used to determine this angular offset and adjust the magnetic field appropriately. The microrobots are located using real-time particle tracking that integrates with a video camera. A target location or desired trajectory can be drawn by the user for the microrobots to follow.

cs.RO↗

arXiv2022

ModMag: A Modular Magnetic Micro-Robotic Manipulation Device

Electromagnetic systems have been used extensively for the control magnetically actuated objects, such as in microrheology and microrobotics research. Therefore, optimizing the design of such systems is highly desired. Some of the features that are lacking in most current designs are compactness, portability, and versatility. Portability is especially relevant for biomedical applications in which in vivo or in vitro testing may be conducted in locations away from the laboratory microscope. This document describes the design, fabrication and implementation of a compact, low cost, versatile, and user friendly device (the ModMag) capable of controlling multiple electromagnetic setups, including a two-dimensional 4-coil traditional configuration, a 3-dimensional Helmholtz configuration, and a 3-dimensional magnetic tweezer configuration. All electronics and circuitry for powering the systems is contained in a compact 10"x6"x3" system which includes a 10" touchscreen. A graphical user interface provides additional ease of use. The system can also be controlled remotely, allowing for more flexibility and the ability to interface with other software running on the remote computer such as propriety camera software. Aside from the software and circuitry, we also describe the design of the electromagnetic coil setups and provide examples of the use of the ModMag in experiments.

cs.RO↗

arXiv2022

Dynamics and Control of Bubble-Propelled Microrobots

Having the advantage of being relatively fast and powerful, as well as readily fabricated, spherical bubble-propelled microrobots are particularly well suited for applications such as cargo delivery, micromanipulation, and biological or environmental remediation. However, there have been limited examples of control and manipulation with these microrobots and few studies on their dynamics. Here we investigate the bubble formation and dynamics of both hemispherically coated Janus microrobots as well as GLAD "patchy" microrobots which not only provide for an interesting comparison, but also exhibit useful properties in their own right. Specifically, we find that the patchy microrobots have a tendency to produce smaller bubbles and undergo smoother motion, properties that are beneficial for applications such as precise micro-manipulation, for example. We demonstrate manipulation and assemble of passive spheres on a substrate as well as at an air-liquid interface. We also characterize the propulsion and bubble formation of both types of microrobots and find that previously proposed theories insufficiently describe their motion and bubble bursting mechanism. Additionally, we observe that the microrobots, which reside at the air-liquid interface, demonstrate positive gravitaxis towards the droplet edges which we attribute to a torque resulting from opposing downward and buoyant forces on the microrobot.

physics.app-ph↗

arXiv2021

A First-Order Approach to Model Simultaneous Control of Multiple Microrobots

The control of swarm systems is relatively well understood for simple robotic platforms at the macro scale. However, there are still several unanswered questions about how similar results can be achieved for microrobots. In this paper, we propose a modeling framework based on a dynamic model of magnetized self-propelling Janus microrobots under a global magnetic field. We verify our model experimentally and provide methods that can aim at accurately describing the behavior of microrobots while modeling their simultaneous control. The model can be generalized to other microrobotic platforms in low Reynolds number environments.

cs.RO↗