arXiv subjects

Saravana Prashanth Murali Babu

Publications and source records attributed to Saravana Prashanth Murali Babu.

6 recordsLinked to original sources

arXiv2026

Tactile Perception through Fluid-Solid Interaction

Soft tactile sensors elevate robotic touch through enhanced flexibility and adaptability, yet most existing designs depend on embedded electronics that are susceptible to interference and environmental limitations. In this work, we leverage fluid-solid interactions to develop a class of soft tactile sensors that operate entirely without electronics at the sensing site. The sensor comprises a fluid-filled elastomeric channel connected to only two external pressure sensors. Touching different regions of the elastomeric surface displaces the viscous fluid, producing distinct pressure patterns that encode both touch position and force. These signals are decoded through a machine learning framework that integrates feature extraction, soft clustering, and adaptive neuro-fuzzy inference to achieve accurate localization and force estimation. We validate this concept through single-point touch localization and force estimation in a linear (1D) sensor and extend the same sensing principle to 2D tactile mapping by routing the channel across the surface using space-filling curves, while maintaining the same minimal hardware setup. This simple approach remains effective in environments where conventional electronic sensors often fail, such as underwater or in the presence of magnetic interference.

cond-mat.soft↗

arXiv2026

SoftPINCH: EMG-Driven Soft Exoskeleton Assistance for Finger Flexion and Grasping

Surface electromyography (sEMG) provides a non-invasive interface for detecting hand-movement intention and controlling wearable assistive devices. However, reliable EMG-driven hand assistance remains challenging because EMG signals are affected by noise, motion artifacts, electrode placement, muscle fatigue, and inter-subject variability. At the same time, many hand exoskeletons remain mechanically restrictive or bulky, limiting comfort and natural hand motion. This work presents SoftPINCH, an EMG-driven soft wearable exoskeleton for thumb-index finger flexion and pinch grasp assistance. The system combines a tendon-driven soft exoskeleton, fingertip magnetic contact sensing, and neural EMG decoding for intention-based assistance. Surface EMG was recorded from forearm muscles during index and thumb movements, and three subject-independent decoding architectures were evaluated: LSTM, CNN+LSTM, and CNN+LSTM with attention. The CNN+LSTM and CNN+LSTM-attention models both achieved 99.4% LOSO test accuracy, outperforming the standalone LSTM, which reached 97.8%. However, the attention mechanism did not provide a significant improvement over CNN+LSTM, indicating that CNN-based feature extraction was sufficient for robust EMG representation. The CNN+LSTM model was therefore selected for real-time deployment due to its high accuracy and lower architectural complexity. Functional evaluation showed that active exoskeleton assistance reduced muscular effort during isolated finger flexion and object grasping. During weighted grasping, assistance reduced muscular effort across all tested loads, with a 92.6% reduction at the highest load. These results demonstrate the potential of SoftPINCH for intuitive, low-effort pinch assistance using real-time EMG-driven soft robotic control.

cs.RO↗

arXiv2026

SoFiE: Soft Finger Exoskeleton for Intelligent Grasping

Soft wearable robotic systems have emerged as a promising solution for assisting individuals with reduced hand function. This paper presents SoFiE, a modular soft finger exoskeleton designed to assist index-finger flexion during grasping tasks. The proposed system is primarily fabricated using 3D-printed flexible materials, enabling a lightweight, low-profile, and modular design. Actuation is achieved through a tendon-driven mechanism powered by a compact DC motor, while passive extension is provided by a compliant conductive spring. This element, termed StretchSense, also functions as a proprioceptive sensor by exhibiting resistance changes under deformation. Furthermore, a novel tactile sensing approach, MagSense, is introduced, using a magnet and magnetometer pair embedded in a soft fingertip structure to estimate contact force and object compliance. The system is fully untethered and controlled by an embedded microcontroller. In addition, actuator-level sensing through motor encoder feedback enables estimation of the system state, providing a foundation for safe and adaptive control strategies. Experimental validation demonstrates the capability of the system to provide reliable pose estimation, distinguish between materials with different stiffness, and generate distinct sensor signatures across different grasping tasks. This paper details the design, fabrication, and sensing concepts of the proposed exoskeleton as a proof of concept toward modular, soft, and assistive wearable robotics.

cs.RO↗

arXiv2025

Tailoring materials into kirigami robots

Kirigami, the traditional paper-cutting craft, holds immense potential for revolutionizing robotics by providing multifunctional, lightweight, and adaptable solutions. Kirigami structures, characterized by their bending-dominated deformation, offer resilience to tensile forces and facilitate shape morphing under small actuation forces. Kirigami components such as actuators, sensors, batteries, controllers, and body structures can be tailored to specific robotic applications by optimizing cut patterns. Actuators based on kirigami principles exhibit complex motions programmable through various energy sources, while kirigami sensors bridge the gap between electrical conductivity and compliance. Kirigami-integrated batteries enable energy storage directly within robot structures, enhancing flexibility and compactness. Kirigami-controlled mechanisms mimic mechanical computations, enabling advanced functionalities such as shape morphing and memory functions. Applications of kirigami-enabled robots include grasping, locomotion, and wearables, showcasing their adaptability to diverse environments and tasks. Despite promising opportunities, challenges remain in the design of cut patterns for a given function and streamlining fabrication techniques.

cs.RO↗

arXiv2025

Weld n'Cut: Automated fabrication of inflatable fabric actuators

Lightweight, durable textile-based inflatable soft actuators are widely used in soft robotics, particularly for wearable robots in rehabilitation and in enhancing human performance in demanding jobs. Fabricating these actuators typically involves multiple steps: heat-sealable fabrics are fused with a heat press, and non-stick masking layers define internal chambers. These layers must be carefully removed post-fabrication, often making the process labor-intensive and prone to errors. To address these challenges and improve the accuracy and performance of inflatable actuators, we introduce the Weld n'Cut platform-an open-source, automated manufacturing process that combines ultrasonic welding for fusing textile layers with an oscillating knife for precise cuts, enabling the creation of complex inflatable structures. We demonstrate the machine's performance across various materials and designs with arbitrarily complex geometries.

cs.RO↗

arXiv2023

Soft robotics towards sustainable development goals and climate actions

Soft robotics technology can aid in achieving United Nations Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world through fostering adaptation, restoration, and remediation. Moreover, the implementation of soft robotics can lead to groundbreaking discoveries in material science, biology, control systems, energy efficiency, and sustainable manufacturing processes. However, to achieve these goals, we need further improvements in understanding biological principles at the basis of embodied and physical intelligence, environment-friendly materials, and energy-saving strategies to design and manufacture self-piloting and field-ready soft robots. This paper provides insights on how soft robotics can address the pressing issue of environmental sustainability. Sustainable manufacturing of soft robots at a large scale, exploring the potential of biodegradable and bioinspired materials, and integrating onboard renewable energy sources to promote autonomy and intelligence are some of the urgent challenges of this field that we discuss in this paper. Specifically, we will present field-ready soft robots that address targeted productive applications in urban farming, healthcare, land and ocean preservation, disaster remediation, and clean and affordable energy, thus supporting some of the SDGs. By embracing soft robotics as a solution, we can concretely support economic growth and sustainable industry, drive solutions for environment protection and clean energy, and improve overall health and well-being.

cs.RO↗