Tag: Lars Bengtsson

Inchworm-Inspired Soft Robot with Groove-Guided Locomotion on ArXiv

Photograph of the soft robot, consisting of a multilayer rolled dielectric elastomer actuator integrated with a
flexible PET sheet. (Image by H. P. Thanabalan.)
Inchworm-Inspired Soft Robot with Groove-Guided Locomotion
Hari Prakash Thanabalan, Lars Bengtsson, Ugo Lafont, Giovanni Volpe
arXiv: 2512.07813

Soft robots require directional control to navigate complex terrains. However, achieving such control often requires multiple actuators, which increases mechanical complexity, complicates control systems, and raises energy consumption. Here, we introduce an inchworm-inspired soft robot whose locomotion direction is controlled passively by patterned substrates. The robot employs a single rolled dielectric elastomer actuator, while groove patterns on a 3D-printed substrate guide its alignment and trajectory. Through systematic experiments, we demonstrate that varying groove angles enables precise control of locomotion direction without the need for complex actuation strategies. This groove-guided approach reduces energy consumption, simplifies robot design, and expands the applicability of bio-inspired soft robots in fields such as search and rescue, pipe inspection, and planetary exploration.

SmartTrap: Automated Precision Experiments with Optical Tweezers on ArXiv

Illustration of three different experiments autonomously performed by the SmartTrap system: DNA pulling experiments (top), red blood cell stretching (bottom left), and particle-particle interaction measurements (bottom right). (Image by M. Selin.)
SmartTrap: Automated Precision Experiments with Optical Tweezers
Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, Giovanni Volpe
arXiv: 2505.05290

There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and  increasing reproducibility. For example, optical tweezers are widely employed in single-molecule biophysics, cell biomechanics, and soft matter physics, but they still require a human operator, resulting in low throughput and limited repeatability. Here, we present a smart optical tweezers platform, which we name SmartTrap, capable of performing complex experiments completely autonomously. SmartTrap integrates real-time 3D particle tracking using
deep learning, custom electronics for precise feedback control, and a microfluidic setup for particle handling. We demonstrate the ability of SmartTrap to operate continuously, acquiring high-precision data over extended periods of time, through a series of experiments. By bridging the gap between manual  experimentation and autonomous operation, SmartTrap establishes a robust and open source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics, and colloidal science with reduced experimental
overhead and operator bias.