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1. Modeling Human Temporal Uncertainty in Human-Agent Teams

   Abo Dominguez, Maya; La, William; Boerkoel, James C. (January 2020, Proceedings of the AI-HRI Symposium at AAAI-FSS 2020)

   null (Ed.)

   Automated scheduling is potentially a very useful tool for facilitating efficient, intuitive interactions between a robot and a human teammate. However, a current gap in automated scheduling is that it is not well understood how to best represent the timing uncertainty that human teammates introduce. This paper attempts to address this gap by designing an online human-robot collaborative packaging game that we use to build a model of human timing uncertainty from a population of crowdworkers. We conclude that heavy-tailed distributions are the best models of human temporal uncertainty, with a Log-Normal distribution achieving the best fit to our experimental data. We discuss how these results along with our collaborative online game will inform and facilitate future explorations into scheduling for improved human-robot fluency. 

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2. Analyzing the Fluency of Human-Robot Interactions

   Weiss, Emily; Jacotin, Zeneve; Jackson, Ryan Blake; Yuan, Amy; Boerkoel, James (January 2023, AAAI 2023 Spring Symposium on HRI in Academia and Industry: Bridging the Gap)

   Fluency---described as the ``coordinated meshing of joint activities between members of a well-synchronized team''---is essential to human-robot team success. Human teams achieve fluency through rich, often mostly implicit, communication. A key challenge in bridging the gap between industry and academia is understanding what influences human perception of a fluent team experience to better optimize human-robot fluency in industrial environments. This paper addresses this challenge by developing an online experiment featuring videos that vary the timing of human and robot actions to influence perceived team fluency. Our results support three broad conclusions. First, we did not see differences across most subjective fluency measures. Second, people report interactions as more fluent as teammates stay more active. Third, reducing delays when humans' tasks depend on robots increases perceived team fluency. 

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3. Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback

   https://doi.org/10.1145/3319502.3374824  

   Wang, Guan; Trimbach, Carl; Lee, Jun Ki; Ho, Mark K.; Littman, Michael L. (March 2020, Human Robot Interaction (HRI'20))

   This paper addresses the problem of training a robot to carry out temporal tasks of arbitrary complexity via evaluative human feedback that can be inaccurate. A key idea explored in our work is a kind of curriculum learning—training the robot to master simple tasks and then building up to more complex tasks. We show how a training procedure, using knowledge of the formal task representation, can decompose and train any task efficiently in the size of its representation. We further provide a set of experiments that support the claim that non-expert human trainers can decompose tasks in a way that is consistent with our theoretical results, with more than half of participants successfully training all of our experimental missions. We compared our algorithm with existing approaches and our experimental results suggest that our method outperforms alternatives, especially when feedback contains mistakes. 

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4. Comparison of CNN and LSTM Networks on Human Intention Prediction in Physical Human-Robot Interactions

   Zadeh, Khosro G; Zendehdel, Niloofar; Holmes, George L; Bonnett, Keyri M; Costa, Amy; Burns, Devin; Leu, Ming C; Song, Yun Seong (August 2025, The Fool)

   Advancements in robotics and AI have increased the demand for interactive robots in healthcare and assistive applications. However, ensuring safe and effective physical human-robot interactions (pHRIs) remains challenging due to the complexities of human motor communication and intent recognition. Traditional physics-based models struggle to capture the dynamic nature of human force interactions, limiting robotic adaptability. To address these limitations, neural networks (NNs) have been explored for force-movement intention prediction. While multi-layer perceptron (MLP) networks show potential, they struggle with temporal dependencies and generalization. Long Short-Term Memory (LSTM) networks effectively model sequential dependencies, while Convolutional Neural Networks (CNNs) enhance spatial feature extraction from human force data. Building on these strengths, this study introduces a hybrid LSTM-CNN framework to improve force-movement intention prediction, increasing accuracy from 69% to 86% through effective denoising and advanced architectures. The combined CNN-LSTM network proved particularly effective in handling individualized force-velocity relationships and presents a generalizable model paving the way for more adaptive strategies in robot guidance. These findings highlight the importance of integrating spatial and temporal modeling to enhance robot precision, responsiveness, and human-robot collaboration. Index Terms —- Physical Human-Robot Interaction, Intention Detection, Machine Learning, Long-Short Term Memory (LSTM) 

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5. Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS

   https://doi.org/10.1038/s41598-021-90624-6  

   Hossen, Md Abir; Diwakar, Prasoon K; Ragi, Shankarachary (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing solution (UMS) to estimate soil TN in an agricultural farm. TN is an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. The ground-truth data required to train the AI approaches is generated via laser-induced breakdown spectroscopy (LIBS), which can be readily used to characterize soil samples, providing rapid chemical analysis of the samples and their constituents (e.g., nitrogen, potassium, phosphorus, calcium). Although LIBS was previously applied for soil nutrient detection, there is no existing study on the integration of LIBS with UAV multispectral imaging and AI. We train two machine learning (ML) models including multi-layer perceptron regression and support vector regression to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red (R), near-infrared, and green (G) spectral bands, computed vegetation indices (NDVI), and environmental variables including air temperature and relative humidity (RH). To generate the ground-truth data or the training data for the machine learning models, we determine the N spectrum of the soil samples (collected from a farm) using LIBS and develop a calibration model using the correlation between actual TN of the soil samples and the maximum intensity of N spectrum. In addition, we extract the features from the multispectral images captured while the UAV follows an autonomous flight plan, at different growth stages of the crops. The ML model’s performance is tested on a fixed configuration space for the hyper-parameters using various hyper-parameter optimization techniques at three different wavelengths of the N spectrum. 

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