arXiv subjects

Thilina Dulantha Lalitharatne

Publications and source records attributed to Thilina Dulantha Lalitharatne.

6 recordsLinked to original sources

arXiv2026

Robotic Fruits with Tunable Stiffness and Sensing: Towards a Methodology for Developing Realistic Physical Twins of Fruits

The global agri-food sector faces increasing challenges from labour shortages, high consumer demand, and supply-chain disruptions, resulting in substantial losses of unharvested produce. Robotic harvesting has emerged as a promising alternative; however, evaluating and training soft grippers for delicate fruits remains difficult due to the highly variable mechanical properties of natural produce. This makes it difficult to establish reliable benchmarks or data-driven control strategies. Existing testing practices rely on large quantities of real fruit to capture this variability, leading to inefficiency, higher costs, and waste. The methodology presented in this work aims to address these limitations by developing tunable soft physical twins that emulate the stiffness characteristics of real fruits at different ripeness levels. A fiber-reinforced pneumatic physical twin of a kiwi fruit was designed and fabricated to replicate the stiffness at different ripeness levels. Experimental results show that the stiffness of the physical twin can be tuned accurately over multiple trials (97.35 - 99.43% accuracy). Gripping tasks with a commercial robotic gripper showed that sensor feedback from the physical twin can reflect the applied gripping forces. Finally, a stress test was performed over 50 cycles showed reliable maintenance of desired stiffness (0.56 - 1.10% error). This work shows promise that robotic physical twins could adjust their stiffness to resemble that of real fruits. This can provide a sustainable, controllable platform for benchmarking and training robotic grippers.

cs.RO↗

arXiv2026

Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients

Misdiagnosis can result in delayed treatment and patient harm. Robotic patient simulators (robopatients) provide a controlled framework for training and evaluating clinicians in rare and complex cases. We investigate auditory tactile congruence in the synthesis of adaptive vocal pain expressions for robopatients. The system generates pain vocalizations in response to tactile stimuli applied during abdominal palpation. Haptic input is captured through an abdominal phantom and processed using an internal palpation-to-pain mapping model that drives acoustic output. To evaluate perceptual congruence between palpation force and synthesized pain expressions, we conducted a study comprising 7,680 trials with 20 participants. Participants rated perceived pain intensity based solely on auditory feedback. We analyzed the influence of acoustic parameters on agreement between applied force and perceived pain. Results indicate that amplitude and pitch significantly affect perceptual agreement, independent of pain sound category. Increased palpation force was associated with higher agreement ratings, consistent with psychophysical scaling effects. Among the tested acoustic features, pitch exerted a stronger influence than amplitude on perceived congruence. These findings demonstrate that modulation of pitch and amplitude is critical for achieving perceptually coherent auditory tactile mappings in robotic pain simulation. The proposed framework supports the development of high fidelity robotic patient simulators and provides a platform for studying multidimensional representations of pain in embodied robotic systems.

cs.RO↗

arXiv2026

Human Emotion-Mediated Soft Robotic Arts: Exploring the Intersection of Human Emotions, Soft Robotics and Arts

Soft robotics has emerged as a versatile field with applications across various domains, from healthcare to industrial automation, and more recently, art and interactive installations. The inherent flexibility, adaptability, and safety of soft robots make them ideal for applications that require delicate, organic, and lifelike movement, allowing for immersive and responsive interactions. This study explores the intersection of human emotions, soft robotics, and art to establish and create new forms of human emotion-mediated soft robotic art. In this paper, we introduce two soft embodiments: a soft character and a soft flower as an art display that dynamically responds to brain signals based on alpha waves, reflecting different emotion levels. We present how human emotions can be measured as alpha waves based on brain/EEG signals, how we map the alpha waves to the dynamic movements of the two soft embodiments, and demonstrate our proposed concept using experiments. The findings of this study highlight how soft robotics can embody human emotional states, offering a new medium for insightful artistic expression and interaction, and demonstrating how art displays can be embodied.

cs.RO↗

arXiv2026

Palpation Alters Auditory Pain Expressions with Gender-Specific Variations in Robopatients

Diagnostic errors remain a major cause of preventable mortality, particularly in resource limited settings. Medical training simulators, including robopatients, help reduce such errors by replicating patient responses during procedures such as abdominal palpation. However, generating realistic multimodal feedback especially auditory pain expressions remains challenging due to the complex, nonlinear relationship between applied palpation forces and perceived pain sounds. The high dimensionality and perceptual variability of pain vocalizations further limit conventional modeling approaches. We propose a novel experimental paradigm for adaptive pain expressivity in robopatients that dynamically generates auditory pain responses to palpation forces using human in the loop machine learning. Specifically, we employ Proximal Policy Optimization (PPO), a reinforcement learning algorithm suited for continuous control, to iteratively refine pain sound generation based on real time human evaluative feedback. The system initializes randomized mappings between force inputs and sound outputs, and the learning agent progressively adjusts them to align with human perceptual preferences. Results show that the framework adapts to individual palpation behaviors and subjective sound preferences while capturing a broad range of perceived pain intensities, from mild discomfort to acute distress. We also observe perceptual saturation at lower force ranges, with gender specific thresholds in pain sound perception. This work demonstrates the feasibility of human in the loop reinforcement learning for co-optimizing haptic input and auditory pain expression in medical simulators, highlighting the potential of adaptive and immersive platforms to enhance palpation training and reduce diagnostic errors.

cs.RO↗

arXiv2020

Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms

Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most commercially available sEMG acquisition systems are not suitable for real-time gesture recognition. In this paper, a method of acquiring sEMG signals using novel low-cost, active, dry-contact, flexible sensors has been proposed. Since the active sEMG sensor was developed to be used along with a bionic arm, the sensor was tested for its ability to acquire sEMG signals that could be used for real-time classification of five selected gestures. In a study of 4 subjects, the average classification accuracy for real-time gesture classification using the active sEMG sensor system was 85%. The common-mode rejection ratio of the sensor was measured to 59 dB, and thus the sensor's performance was not substantially limited by its active circuitry. The proposed sensors can be interfaced with a variety of amplifiers to perform fully wearable sEMG acquisition. This satisfies the need for a low-cost sEMG acquisition system for prostheses.

cs.ET↗

arXiv2020

Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel sEMG Signals

Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.

eess.SP↗