Trevor W. Exley

Istituto Italiano di Tecnologia

Via Morego, 30, 16163 Genova GE, Italy

Email: trevor.exley [at] iit.it

I am a postdoctoral researcher at the Istituto Italiano di Tecnologia (IIT) in Genova, advised by Lucia Beccai in the Soft Biorobotics Perception (SBRP) Lab. My current work focuses on elephant-inspired soft robotic manipulators and monolithic systems, exploring integrated actuation, sensing, and control for embodied intelligence in compliant robots.

Previously, I completed my Ph.D. in Biomedical Engineering at the University of North Texas, where I was a member of the Advanced Robotic Manipulators (ARM) Lab under Amir Jafari. My doctoral research investigated thermo-active strategies for soft actuators and thermal variable-impedance systems, advancing their design for safe, adaptive physical human–robot interaction. The work introduced compliant phase-change actuators and thermoelectric control approaches that improve efficiency, adaptability, and thermal responsiveness in soft robotic devices. I was an NIH T32 Fellow and supported through the Graduate Research Training Initiative for Student Enhancement (G-RISE) program.

selected publications

2025

MELEGROS: Monolithic Elephant-inspired Gripper with Optical Sensors

Petr Trunin, Diana Cafiso, Anderson Brazil Nardin, Trevor Exley, and Lucia Beccai

2025

Abs

The elephant trunk exemplifies a natural gripper where structure, actuation, and sensing are seamlessly integrated. Inspired by the distal morphology of the African elephant trunk, we present MELEGROS, a Monolithic ELEphant-inspired GRipper with Optical Sensors, emphasizing sensing as an intrinsic, co-fabricated capability. Unlike multi-material or tendon-based approaches, MELEGROS directly integrates six optical waveguide sensors and five pneumatic chambers into a pneumatically actuated lattice structure (12.5 mm cell size) using a single soft resin and one continuous 3D print. This eliminates mechanical mismatches between sensors, actuators, and body, reducing model uncertainty and enabling simulation-guided sensor design and placement. Only four iterations were required to achieve the final prototype, which features a continuous structure capable of elongation, compression, and bending while decoupling tactile and proprioceptive signals. MELEGROS (132 g) lifts more than twice its weight, performs bioinspired actions such as pinching, scooping, and reaching, and delicately grasps fragile items like grapes. The integrated optical sensors provide distinct responses to touch, bending, and chamber deformation, enabling multifunctional perception. MELEGROS demonstrates a new paradigm for soft robotics where fully embedded sensing and continuous structures inherently support versatile, bioinspired manipulation.

2024

Agonist-Antagonist Pouch Motors: Bidirectional Soft Actuators Enhanced by Thermally Responsive Peltier Elements

Trevor Exley, Rashmi Wijesundara, Nathan Tan, Akshay Sunkara, Xinyu He, Shuopu Wang, Bonnie Chan, Aditya Jain, Luis Espinosa, and Amir Jafari

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2024

2022

Predicting UPDRS Motor Symptoms in Individuals With Parkinson’s Disease From Force Plates Using Machine Learning

Trevor Exley, Sarah Moudy, Rita M. Patterson, Joonghyun Kim, and Mark V. Albert

IEEE Journal of Biomedical and Health Informatics, Jul 2022

Abs

Parkinson’s disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinson’s Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e., the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.