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Qiao Wang

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

Contactless and Economical Chemical Reaction Platform Based on Ultrasonic Field

Yunsheng Li
Qiao Wang
Yuyan Liu
Bo Yuan
Zhuo Chen
Qiang Huang 0002
Tatsuo Arai
Xiaoming Liu 0007

Chemical reactions constitute a cornerstone of fundamental scientific inquiry, yet traditional methodologies and platforms are encumbered by excessive reagent and consumable demands. Emerging alternatives, such as microfluidic systems, while innovative, suffer from intricate fabrication processes and elevated costs associated with operator training. Other contemporary approaches face limitations including reagent compatibility constraints and prohibitively expensive instrumentation. To address these challenges, this study introduces a contactless chemical reaction platform leveraging an ultrasonic vortex field to achieve stable capture, microscale droplet transport, and sequential multi-droplet mixing without direct contact. This platform substantially reduces contamination risks, minimizes reagent and consumable usage, accommodates a broad spectrum of reagent types, and imposes minimal demands on operator expertise. Demonstrating robust performance in microdose reaction control, the system offers significant potential for advancing chemical research and its applications.

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ICRA Conference 2024 Conference Paper

Comparison of Rating Scale and Pairwise Comparison Methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Qiao Wang
Ziqi Wang
Marc G. Carmichael
Dikai Liu
Chin-Teng Lin

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity nature. Individual variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

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ICRA Conference 2023 Conference Paper

Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration

Qiao Wang
Dikai Liu
Marc G. Carmichael
Chin-Teng Lin

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

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AAAI Conference 2020 Conference Paper

Learning Geo-Contextual Embeddings for Commuting Flow Prediction

Zhicheng Liu
Fabio Miranda
Weiting Xiong
Junyan Yang
Qiao Wang
Claudio Silva

Predicting commuting ﬂows based on infrastructure and landuse information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting ﬂows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the inﬂuence of nearby regions. To address these issues, we propose Geocontextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting ﬂow prediction. Speciﬁcally, we ﬁrst construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. To enhance the effectiveness of the embedding representation, a multitask learning framework is used to introduce stronger restrictions, forcing the embeddings to encapsulate effective representation for ﬂow prediction. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting ﬂows. We evaluate our model using real-world dataset from New York City and the experimental results demonstrate the effectiveness of our proposed method against the state of the art.

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