Publications

Handbook of Soft Robotics

in production Springer Nature, 2024

Write the autonomous control in continuum robots section for chapter on meallable robots for Spring nature, Handbook of Soft Robotics. The book is expected to be published next year.

Handbook of Soft Robotics: Thrishantha Nanayakkara, Jamie Paik, Barbara Mazzolai, Cecilia Laschi, Manu Srivastava, Ian Walker, Shinichi Hirai, Christian Duriez, Isuru Godage, Hunter Gilbert, Angus Clark;Alex Ranne;Xinran Wang; Nicolas Rojas, Hadi Sadati, Ali Starbanov

Cossrat Rod Modeling and Validation for a Soft Continuum Robot with Self-Controllable Variable Curvature

Accepted to 7th IEEE International Conference on Soft Robotics, 2024

This paper introduces a Cossrat modeling for a soft continuum robot with Self-Controllable Variable Curvature, analysis and validation the mathmatical model. The results are validated against real experiments

Xinran Wang and Nicolas Rojas. “ Cossrat Rod Modeling and Validation for a Soft Continuum Robot with Self-Controllable Variable Curvature”, 7th IEEE International Conference on Soft Robotics, Submitted in November 2023.

A Soft Continuum Robot with Self-Controllable Variable Curvature

Accpeted to IEEE Robotics and Automation Letter, 2024

This paper introduces a new type of soft continuum robot which is capable of self-controlling continuously its curvature at the segment level; in contrast to previous designs which either require external forces or machine elements, or whose variable curvature capabilities are discrete—depending on the number of locking mechanisms and segments.

Xinran Wang, Lu Qiujie, Dongmyoung Lee, Zhongxue Gan, and Nicolas Rojas. “A Soft Continuum Robot with Self-Controllable Variable Curvature.” IEEE Robotic and Automation Letter, Accepted in January 2024.

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 2022

This paper presents a model-based learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN).

Xinran, Wang, and Nicolas Rojas. "A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots." In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), pp. 247-254. IEEE, 2022 https://arxiv.org/abs/2204.10454

Design and analysis of a variable inertia spatial robotic tail for dynamic stabilization

Biomimetics, 2020

This paper presents the design of a four degree-of-freedom (DoF) spatial tail and demonstrates the dynamic stabilization of a bipedal robotic platform through a hardware-in-loop simulation.

Xinran Wang, Hailin Ren, Anil Kumar, and Pinhas Ben-Tzvi. "Design and analysis of a variable inertia spatial robotic tail for dynamic stabilization." Biomimetics 5, no. 4 (2020): 55. https://www.mdpi.com/2313-7673/5/4/55f

Parallel deep learning ensembles for human pose estimation

ASME Dynamic Systems and Control Conference, 2018

This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network.

Ren, Hailin, Anil Kumar, Xinran Wang, and Pinhas Ben-Tzvi. “Parallel deep learning ensembles for human pose estimation”. In Dynamic Systems and Control Conference, vol. 51890, p. V001T07A005. American Society of Mechanical Engineers, 2018. https://asmedigitalcollection.asme.org/DSCC/proceedings/DSCC2018/51890/V001T07A005/455561