Author name cluster

Ya-Yen Tsai

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6 papers

2 author rows

IROS Conference 2022 Conference Paper

Multi-fingered Tactile Servoing for Grasping Adjustment under Partial Observation

Hanzhong Liu
Bidan Huang
Qiang Li 0001
Yu Zheng 0001
Yonggen Ling
Wang Wei Lee
Yi Liu 0068
Ya-Yen Tsai

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized multi-fingered robotic hand to collaboratively servo the fingers and palm to achieve the desired grasp. We demonstrate the performance of our method through simulation and physical experiments by having a robot grasp different objects under conditions of variable uncertainty. The results show that our approach achieved a higher success rate and tolerated greater uncertainty than an open-looped grasp.

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JBHI Journal 2021 Journal Article

Counting Bites and Recognizing Consumed Food from Videos for Passive Dietary Monitoring

Jianing Qiu
Frank P.-W. Lo
Shuo Jiang
Ya-Yen Tsai
Yingnan Sun
Benny Lo

Assessing dietary intake in epidemiological studies are predominantly based on self-reports, which are subjective, inefficient, and also prone to error. Technological approaches are therefore emerging to provide objective dietary assessments. Using only egocentric dietary intake videos, this work aims to provide accurate estimation on individual dietary intake through recognizing consumed food items and counting the number of bites taken. This is different from previous studies that rely on inertial sensing to count bites, and also previous studies that only recognize visible food items but not consumed ones. As a subject may not consume all food items visible in a meal, recognizing those consumed food items is more valuable. A new dataset that has 1, 022 dietary intake video clips was constructed to validate our concept of bite counting and consumed food item recognition from egocentric videos. 12 subjects participated and 52 meals were captured. A total of 66 unique food items, including food ingredients and drinks, were labelled in the dataset along with a total of 2, 039 labelled bites. Deep neural networks were used to perform bite counting and food item recognition in an end-to-end manner. Experiments have shown that counting bites directly from video clips can reach 74. 15% top-1 accuracy (classifying between 0-4 bites in 20-second clips), and a MSE value of 0. 312 (when using regression). Our experiments on video-based food recognition also show that recognizing consumed food items is indeed harder than recognizing visible ones, with a drop of 25% in F1 score.

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IROS Conference 2021 Conference Paper

Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory

Zihan Ding
Ya-Yen Tsai
Wang Wei Lee
Bidan Huang

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the incorporation of tactile sensing into RL for contact-rich tasks lacks investigation. In this paper, we model a tactile sensor in simulation and study the effects of its feedback in RL-based robotic control via a zero-shot sim-to-real approach with domain randomization. We demonstrate that learning and controlling with feedback from tactile sensor arrays at the gripper, both in simulation and reality, can enhance grasping stability, which leads to a significant improvement in robotic manipulation performance for a door opening task. In real-world experiments, the door open angle was increased by 45% on average for transferred policies with tactile sensing over those without it.

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IROS Conference 2020 Conference Paper

A Novel Endoscope Design Using Spiral Technique for Robotic-Assisted Endoscopy Insertion

Wei Li 0105
Ya-Yen Tsai
Guang-Zhong Yang
Benny Lo

Gastrointestinal (GI) endoscopy is a conventional and prevalent procedure used to diagnose and treat diseases in the digestive tract. This procedure requires inserting an endoscope equipped with a camera and instruments inside a patient to the target of interest. To manoeuvre the endoscope, an endoscopist would rotate the knob at the handle to change the direction of the distal tip and apply the feeding force to advance the endoscope. However, due to the nature of the design, this often causes a looping problem during insertion making it difficult to be further advanced to the deeper section of the tract such as the transverse and ascending colon. To this end, in this paper, we propose a novel robotic endoscope which is covered by a rotating screw-like sheath and uses a spiral insertion technique to generate 'pull' forces at the distal tip of the endoscope to facilitate insertion. The whole shaft of the endoscope can be actively rotated, providing the crawling ability from the attached spiral sheath. With the redundant control on a spring-like continuum joint, the bending tip is capable of maintaining its orientation to assist endoscope navigation. To test its functions and feasibility to address the looping problem, three experiments were carried out. The first two experiments were to analyse the kinematic of the device and test the ability of the device to hold its distal tip at different orientation angles during spiral insertion. In the third experiment, we inserted the device in the bent colon phantom to evaluate the effectiveness of the proposed design against looping when advancing through a curved section of a colon. Results show the moving ability using spiral technique and verify its potential of clinical application.

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ICRA Conference 2019 Conference Paper

Transfer Learning for Surgical Task Segmentation

Ya-Yen Tsai
Bidan Huang
Yao Guo 0002
Guang-Zhong Yang

In this paper, we present a novel approach for surgical task segmentation. A segmentation policy learns the correlations between features and segmentation points from manually labeled data. The most correlated features and rules for segmenting them are identified and learned. These form a complete set of segmentation policy. The proposed approach is developed to segment new but similar tasks through transfer learning. It is verified through applying the segmentation rule learned from the labeled data to segment other tasks. The performance of the proposed algorithm was evaluated by comparing the results against the ground truths. Experimental results demonstrate that our approach can achieve high segmentation rates with an accuracy of between 68. 8% - 81. 8%.

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IROS Conference 2019 Conference Paper

Unsupervised Task Segmentation Approach for Bimanual Surgical Tasks using Spatiotemporal and Variance Properties

Ya-Yen Tsai
Yao Guo 0002
Guang-Zhong Yang

In surgical workflow analysis and training in robot-assisted surgery, automatic task segmentation could significantly reduce the manual labeling time and enhance robot learning efficiency. This paper presents an unsupervised segmentation approach to automatically segment a given surgical task without manual intervention. A new segmentation method is presented, which relies only on bimanual kinematic trajectories without the need for prior information about the data. Specifically, surgical tasks are segmented by fusing trajectories’ spatiotemporal and variance properties. To demonstrate the effectiveness of the proposed method, detailed experiments were first conducted on our dataset. We segmented trajectories of three different surgical stitches and observed an average F 1 score of 77. 9% against the ground truths. The same trajectories were then added with different levels of noises and the segmentation comparison was made with four other methods. The proposed algorithm had demonstrated its robustness against the noises. Finally, to assess its generalization ability, the method was evaluated on publicly available JIGSAWS dataset and an average F 1 score of 75. 5% was achieved.

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