Dr. Ricardo Vilela de Godoy

Dr. Ricardo Vilela de Godoy

I am a postdoctoral researcher at the Mobile Robotics Group at the University of São Paulo, Brazil. I've been working with four-legged robots with robotic arms and grippers for intuitive teleoperation and dexterous manipulation.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Offline Versus Real-Time Grasp Prediction Employing a Wearable High-Density Lightmyography Armband: On the Control of Prosthetic Hands

Published in IEEE Access, 2025

In this study, a wearable high-density lightmyography armband is proposed, and the offline and real-time grasp prediction schemes are compared in an attempt to deepen our understanding in real-time decoding employing lightmyography signals.

Recommended citation: B. Guan, R. V. Godoy, M. Shahmohammadi, A. Dwivedi and M. Liarokapis, "Offline Versus Real-Time Grasp Prediction Employing a Wearable High-Density Lightmyography Armband: On the Control of Prosthetic Hands," in IEEE Access, vol. 13, pp. 60672-60683, 2025, doi: 10.1109/ACCESS.2025.3556920.
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Multi-Layer, Sensorized Kirigami Grippers for Delicate Yet Robust Robot Grasping and Single-Grasp Object Identification

Published in IEEE Access, 2024

In this paper, we explore this new class of soft robotic grippers by proposing new designs and investigating their post-contact reconfiguration behaviour in a series of experiments covering grasping experiments and grasping force exertion measurement experiments.

Recommended citation: J. Buzzatto et al., "Multi-Layer, Sensorized Kirigami Grippers for Delicate Yet Robust Robot Grasping and Single-Grasp Object Identification," in IEEE Access, vol. 12, pp. 115994-116012, 2024, doi: 10.1109/ACCESS.2024.3446729.
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Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch

Published in IEEE Access, 2023

In this work we compare the performance of EMG-based hand gesture decoding models developed using three learning approaches.

Recommended citation: R. V. Godoy, B. Guan, F. Sanches, A. Dwivedi and M. Liarokapis, "Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch," in IEEE Access, vol. 11, pp. 104142-104154, 2023, doi: 10.1109/ACCESS.2023.3317956.
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On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces

Published in IEEE Access, 2023

In this work, we introduce a new muscle-machine interfacing technique called lightmyography (LMG), that can be used to efficiently decode human hand gestures, motion, and forces from the detected contractions of the human muscles.

Recommended citation: Shahmohammadi, Mojtaba, et al. "On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces." Scientific Reports 13.1 (2023): 327.
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On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes

Published in IEEE ACCESS, 2022

In this work, we compare various machine learning and feature extraction methods for the creation of EMG based control frameworks for dexterous robotic telemanipulation.

Recommended citation: R. V. Godoy, A. Dwivedi, B. Guan, A. Turner, D. Shieff and M. Liarokapis, "On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes," in IEEE Access, vol. 10, pp. 99661-99674, 2022, doi: 10.1109/ACCESS.2022.3206436.
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Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers

Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022

In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers.

Recommended citation: R. V. Godoy, A. Dwivedi and M. Liarokapis, "Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2207-2216, 2022, doi: 10.1109/TNSRE.2022.3196622.
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Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers

Published in IEEE RA-L, 2022

In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands. The performance of this method is evaluated and compared with other well-known methods, employing the open-access Ninapro dataset.

Recommended citation: R. V. Godoy et al., "Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10200-10207, Oct. 2022, doi: 10.1109/LRA.2022.3192623.
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Conference Papers

A Video Dataset of Everyday Life Grasps for the Training of Shared Control Operation Models for Myoelectric Prosthetic Hands

Published in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024

In this paper, we present a new dataset capturing user interactions with a wide variety of everyday life objects using a fully actuated, human-like robot hand and an onboard camera.

Recommended citation: R. V. Godoy, B. Guan, A. Dwivedi, M. Owen and M. Liarokapis, "A Video Dataset of Everyday Life Grasps for the Training of Shared Control Operation Models for Myoelectric Prosthetic Hands," 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2024, pp. 1-5, doi: 10.1109/EMBC53108.2024.10782638.
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On Semi-Autonomous Robotic Telemanipulation Employing Electromyography Based Motion Decoding and Potential Fields

Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

This paper proposes a semi-autonomous framework for robotic telemanipulation that employs Electromyography (EMG) based motion decoding and potential fields to execute complex object stacking tasks with a dexterous robot arm-hand system.

Recommended citation: B. Guan, R. V. Godoy, F. Sanches, A. Dwivedi and M. Liarokapis, "On Semi-Autonomous Robotic Telemanipulation Employing Electromyography Based Motion Decoding and Potential Fields," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6991-6997, doi: 10.1109/IROS55552.2023.10342155.
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Scalable. Intuitive Human to Robot Skill Transfer with Wearable Human Machine Interfaces: On Complex, Dexterous Tasks

Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

In this work, we propose an efficient skill transfer methodology comprising intuitive interfaces, efficient optical tracking systems, and compliant control of robotic arm-hand systems.

Recommended citation: F. Sanches et al., "Scalable. Intuitive Human to Robot Skill Transfer with Wearable Human Machine Interfaces: On Complex, Dexterous Tasks," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6318-6325, doi: 10.1109/IROS55552.2023.10341661.
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Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation

Recommended citation: J. Liang et al., "Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6433-6440, doi: 10.1109/IROS55552.2023.10341390.
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An Affordances and Electromyography Based Telemanipulation Framework for Control of Robotic Arm-Hand Systems

Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

In this paper, we propose an intuitive, affordances-oriented EMG-based telemanipulation framework for a robot arm-hand system that allows for dexterous control of the device.

Recommended citation: R. V. Godoy, B. Guan, A. Dwivedi and M. Liarokapis, "An Affordances and Electromyography Based Telemanipulation Framework for Control of Robotic Arm-Hand Systems," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6998-7004, doi: 10.1109/IROS55552.2023.10341955.
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An Adaptive, Humanlike Prosthetic Hand Equipped with a Series Elastic Differential and a Lightmyography Based Control Interface

Published in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023

In this paper, we propose an anthropomorphic, light-weight, and affordable prosthetic hand equipped with a five output, series elastic differential mechanism and an armband utilizing a new muscle machine interfacing method that is called Lightmyography (LMG).

Recommended citation: M. Shahmohammadi, B. Guan, R. V. Godoy and M. Liarokapis, "An Adaptive, Humanlike Prosthetic Hand Equipped with a Series Elastic Differential and a Lightmyography Based Control Interface," 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 2023, pp. 1-7, doi: 10.1109/CASE56687.2023.10260643.
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Multi-Grasp Classification for the Control of Robot Hands Employing Transformers and Lightmyography Signals

Published in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023

In this study, we extend our previous work experimentally validating the efficiency of the LMG armband in classifying thirty-two different gestures from six participants using a deep learning technique called Temporal Multi-Channel Vision Transformers (TMC-ViT)

Recommended citation: R. V. Godoy, B. Guan, A. Dwivedi, M. Shahmohammadi, M. Owen and M. Liarokapis, "Multi-Grasp Classification for the Control of Robot Hands Employing Transformers and Lightmyography Signals," 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, pp. 1-6, doi: 10.1109/EMBC40787.2023.10340274.
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On Human Grasping and Manipulation in Kitchens: Automated Annotation, Insights, and Metrics for Effective Data Collection

Published in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023

In this work, we focus on a comprehensive data collection and analysis of key attributes involved in the selection of grasping and manipulation strategies for the successful execution of kitchen tasks.

Recommended citation: N. Elangovan et al., "On Human Grasping and Manipulation in Kitchens: Automated Annotation, Insights, and Metrics for Effective Data Collection," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 11329-11335, doi: 10.1109/ICRA48891.2023.10161171.
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Comparing Human and Robot Performance in the Execution of Kitchen Tasks: Evaluating Grasping and Dexterous Manipulation Skills

Published in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), 2022

In this work, we focus on comparing human and robot performance in the execution of complex kitchen tasks, assessing the grasping and dexterous manipulation skills that are required.

Recommended citation: N. Elangovan et al., "Comparing Human and Robot Performance in the Execution of Kitchen Tasks: Evaluating Grasping and Dexterous Manipulation Skills," 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), Ginowan, Japan, 2022, pp. 518-525, doi: 10.1109/Humanoids53995.2022.10000248.
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Lightmyography Based Decoding of Human Intention Using Temporal Multi-Channel Transformers

Published in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

In this work, we employ two novel deep learning techniques called Temporal Multi-Channel Transformer (TMC-T) and Temporal Multi-Channel Vision Transformer (TMC-ViT) for the classification of hand gestures based on the LMG data

Recommended citation: R. V. Godoy, A. Dwivedi, M. Shahmohammadi and M. Liarokapis, "Lightmyography Based Decoding of Human Intention Using Temporal Multi-Channel Transformers," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 6087-6094, doi: 10.1109/IROS47612.2022.9981514.
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Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers

Published in 2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 2022

In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands.

Recommended citation: R. V. Godoy et al., "Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers," 2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Korea, Republic of, 2022, pp. 1-8, doi: 10.1109/BioRob52689.2022.9925307.
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Redundant Robot Kinematics Error Analysis for Neurosurgical Procedures

Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

This paper explores the kinematic relation between the robot configuration in joint space and both the robot tool center point (TCP) position resolution and the robot end-effector orientation resolution with the purpose of reducing error.

Recommended citation: R. V. Godoy et al., "Redundant Robot Kinematics Error Analysis for Neurosurgical Procedures," 2021 14th IEEE International Conference on Industry Applications (INDUSCON), São Paulo, Brazil, 2021, pp. 1029-1035, doi: 10.1109/INDUSCON51756.2021.9529675.
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Deep Reinforcement Learning Control of an Autonomous Wheeled Robot in a Challenge Task: Combined Visual and Dynamics Sensoring

Published in 2019 19th International Conference on Advanced Robotics (ICAR), 2019

This paper presents a Deep Reinforcement Learning agent for a 4-wheeled rover in a multi-goal competition task, under the influence of noisy GPS measurements.

Recommended citation: L. A. Marão, L. Casteluci, R. Godoy, H. Garcia, D. V. Magalhães and G. Caurin, "Deep Reinforcement Learning Control of an Autonomous Wheeled Robot in a Challenge Task: Combined Visual and Dynamics Sensoring," 2019 19th International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil, 2019, pp. 368-373, doi: 10.1109/ICAR46387.2019.8981598.
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Preprint Papers

MIHRaGe: A Mixed-Reality Interface for Human-Robot Interaction via Gaze-Oriented Control

Published in arXiv, 2025

This paper presents the MIHRAGe interface, an integrated system that combines gaze-tracking, robotic assistance, and a mixed-reality to create an immersive environment for controlling the robot using only eye movements.

Recommended citation: Baptista, Rafael R., Nina R. Gerszberg, Ricardo V. Godoy, and Gustavo JG Lahr. "MIHRaGe: A Mixed-Reality Interface for Human-Robot Interaction via Gaze-Oriented Control." arXiv preprint arXiv:2505.03929 (2025).
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Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets

Published in arXiv, 2025

This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars.

Recommended citation: Lahr, G. J., Godoy, R. V., Segreto, T. H., Savazzi, J. O., Ajoudani, A., Boaventura, T., & Caurin, G. A. (2025). Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets. arXiv preprint arXiv:2505.03917.
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A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation

Published in arXiv, 2025

In this paper, we collected 640 high-resolution images from a commercial farm spanning multiple growth stages, weed pressures, and lighting variations.

Recommended citation: Segreto, Thiago H., Juliano Negri, Paulo H. Polegato, João Manoel Herrera Pinheiro, Ricardo Godoy, and Marcelo Becker. "A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation." arXiv preprint arXiv:2503.01605 (2025).
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EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

Published in arXiv, 2022

In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals.

Recommended citation: Godoy, Ricardo V., Tharik JS Reis, Paulo H. Polegato, Gustavo JG Lahr, Ricardo L. Saute, Frederico N. Nakano, Helio R. Machado, Americo C. Sakamoto, Marcelo Becker, and Glauco AP Caurin. "EEG-based epileptic seizure prediction using temporal multi-channel transformers." arXiv preprint arXiv:2209.11172 (2022).
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