arXiv subjects

Siddhant Mohapatra

Publications and source records attributed to Siddhant Mohapatra.

3 recordsLinked to original sources

arXiv2026

Run and tumble dynamics of a soft robotic cell

The continuous regulation of transport properties through softness remains a longstanding challenge in active matter. Here, we show that encasing a programmable active particle within a deformable membrane naturally gives rise to intermittent stop-and-go dynamics, with ballistic motion at short times crossing over to diffusion at long times. Crucially, membrane softness acts as a single control parameter that continuously tunes persistence, intermittency, and long-time transport, linking the internal driving to the emergent locomotion of the synthetic cell. Combining experiments, simulations, and a run-and-tumble theoretical framework, we identify the minimal physical ingredients underlying this behavior and establish design principles for programmable soft active transport, opening new avenues at the interface of active matter physics and synthetic robotics.

cond-mat.soft↗

arXiv2026

Physics-informed digital twin and onboard control of a brainbot for intelligent active matter

Establishing adaptive particles that sense their state, anticipate their evolution, and compute control inputs onboard has been a major challenge in non-equilibrium physics. We address this challenge by realizing an autonomous brainbot, building on a recently developed programmable bristlebot. First, we construct a physics-informed digital twin of the device, based on a kinematic model that reproduces measured trajectory statistics and generates long, statistically faithful synthetic trajectories. The kinematics forms the foundation for implementing onboard model predictive control (MPC), enabling autonomous trajectory tracking, demonstrated by accurate execution of a non-trivial target path. This provides a proof of principle for a brainbot that senses its state, predicts its evolution, and computes control inputs onboard, unlike conventional active particles with fixed motility, thereby transforming the brainbot into an agentic physical entity. By integrating physical modeling, data-driven parameter identification, and control into a unified framework, our approach provides a scalable platform for machine-learning-enabled multi-agent studies and lays the groundwork for intelligent, adaptive active matter.

cond-mat.soft↗

arXiv2025

Mixing of a binary passive particle system using smart active particles

Controlled activity of active entities interacting with a passive environment can generate emergent system-level phenomena, positioning such systems as promising platforms for potential downstream applications in targeted drug delivery, adaptive and reconfigurable materials, microfluidic transport and related fields. The present work aims to realise an optimal mixing of two segregated species of passive particles by introducing a small fraction of active particles (2% by composition) with adaptive and intelligent behaviour, directed by a trained Artificial Neural Network-based agent. While conventional run-and-tumble particles can induce mixing in the system, the smart active particles demonstrate superior performance, achieving faster and more efficient mixing. Interestingly, an optimal mixing strategy doesn't involve a uniform dispersion of active particles in the domain, but rather limiting their motion to an eccentrically placed zone of activity, inducing a global rotational motion of the passive particles about the system centre. A transition in the directionality of the passive particles' motion is observed along the radius towards the centre, likening the active particles' motion to an ellipse-shaped void with a defined surface speed. Situated at the intersection of active matter and machine learning, this work highlights the potential of integrating adaptive learning frameworks into traditional active matter models.

cond-mat.soft↗