arXiv subjects

Saitarun Nadipineni

Publications and source records attributed to Saitarun Nadipineni.

4 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↗