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Congestion in urban spaces sometimes leads to serious accidents. To prevent a congestion and serious accident, it is important to properly arrange-building layouts and guide pedestrians safely based on, mathematical modeling and simulation of pedestrian flows. In the real world, however, each pedestrian&#39;s behavior is so complicated and often includes spatio-temporally multiscale decision making while most of existing studies mainly focused on a crossing area in front of a station such as Shibuya scramble crossing. In a previous report, a trained model could simulate some pedestrian trajectory, but it also generated undesirable behavior that a pedestrian ignored a traffic light. In this study, we introduce an attention mechanism to solve this problem. The performance of the model is evaluated quantitatively by the prediction error and qualitatively by visualizing the time of a traffic light switching.

IEEE RO-MAN 2024

Spatio-Temporal Multi-Scale Pedestrian Flow Model by Using Attention Module

human flow

attention

lstm

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Congestion in urban spaces sometimes leads to serious accidents. To prevent a congestion and serious accident, it is important to properly arrange-building layouts and guide pedestrians safely based on, mathematical modeling and simulation of pedestrian flows. In the real world, however, each pedestrian's behavior is so complicated and often includes spatio-temporally multiscale decision making while most of existing studies mainly focused on a crossing area in front of a station such as Shibuya scramble crossing. In a previous report, a trained model could simulate some pedestrian trajectory, but it also generated undesirable behavior that a pedestrian ignored a traffic light. In this study, we introduce an attention mechanism to solve this problem. The performance of the model is evaluated quantitatively by the prediction error and qualitatively by visualizing the time of a traffic light switching.

technical paper

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Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

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The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Avatar robots are telepresence robots that allow people to project themselves physically in distant places. Teleoperators use such avatars to have remote social interactions. Mobility adds to their interactive capabilities. However, it also presents challenges in social spaces; for instance, teleoperators can mistakenly move their avatars in a socially inappropriate manner. Since these robots have yet to become ubiquitous, there is little real-world data to objectively analyze the presence and frequency of such undesirable movement. Consequently, the different ways such movements manifest are unknown, and their occurrence cannot be detected. To address this problem, we conducted a laboratory experiment with hired participants in a replica artwork exhibition so that we could gather realistic human-avatar interaction data using sensors. Our data analysis helped us identify three types of inadvertently inappropriate movement behavior. We present a definition of such behavior based on these three inappropriate movement types and a labeled dataset containing more than 400 interactions, 18% of which were inappropriate. We also present detection models for each inappropriate movement type trained using our dataset.

Identifying and Detecting Inadvertent Socially Inappropriate Movement of Avatar Robots

As end users interact with increasing numbers of autonomous or semi-autonomous systems, collaboration and supervision become more complicated. People must simultaneously manage multiple systems, and most current interfaces do not scale. Augmented Reality (AR) offers a promising solution by placing information over the real world, allowing users to concurrently track the scene and robot(s) -- potentially improving scaling for these devices. In this work, we leverage user gaze - a powerful indicator of attention suitable for reactive systems - to lower cognitive burden and improve performance such that scaling to multiple agents is possible. Gaze is probed in two modalities. In active mode, the user looks at a menu and presses a button to request additional information. The passive mode provides more information when the users gaze dwells on the menu. We performed two studies: 1) participants complete a visual search task with increasing numbers of virtual robotic agents and 2) participants must track the dynamic status of a team with physical agents. Results from the first study show that the passive and active interfaces provide better scaling compared to a non-interactive interface as the number of robots increases. In both studies, users preferred the passive mode, citing a lower mental demand, effort, and frustration.

Gaze-Based Augmented Reality Interfaces to Support Scalable Human-Robot Teaming

This paper introduces a voice-controlled Human Machine Interface (HMI) tailored for an assistive robotic exoskeleton glove, aimed at assisting patients coping with Brachial Plexus Injuries (BPI) in regaining their lost grasping functionality. The development of this HMI draws upon clinical experimentation results, forming a foundation for its design. The paper delves into the challenges encountered while employing a prior voice-based HMI, which necessitated an internet connection for complex computations and exhibited limitations in effectively processing concise commands. To address these issues, an innovative voice-controlled HMI system is proposed, featuring fixed-word detection to replace the speech-to-text (STT) converter and the Neutral Language Processor (NLP) to reduce computational overhead. Furthermore, the new HMI replaces the previous text-independent speaker verification with a text-dependent, one-shot learning approach. This enhancement streamlines custom retraining, significantly improving speaker verification accuracy for concise commands. Experimental results substantiate the applicability of the proposed voice-controlled HMI for assisting individuals with BPI through specialized exoskeleton gloves.

Voice-Controlled Human-Machine Interface for an Assistive Exoskeleton Glove Aiding Patients with Brachial Plexus Injuries

In this paper, we improve the robustness of a multi-camera gesture recognition system in multi-person situations by identifying and tracking the operator. This system is meant as an intelligent room to operate and interact with surrounding devices based on pointing gestures. In the method, we identify the operator by a hand-raising gesture, followed by tracking using the coordinates of the center of the operator's head and extracting only the operator's whole body. From the experimental results, we confirmed that highly accurate tracking could be performed in a multi-person situation of 2 to 5 persons, and that the success rate of extracting images of the operator's whole body was more than 70%. We also clarified issues in the operator identification process and the extraction process.

Robust Gesture-Based Appliance Control Via Operator Identification and Tracking

In recent years, labor accidents and a shortage of skilled workers due to an aging population have become significant issues at construction sites in Japan. To address these challenges, we are developing a Cyber-Physical System (CPS) platform called ROS2-TMS for Construction, which aims to improve both the efficiency and safety of earthwork operations. In this study, we propose a task management system for construction machinery using an open platform named OPERA as an additional function of ROS2-TMS for Construction. This task management system controls construction machinery using environmental information stored in a database, which collects and stores data from sensors deployed throughout the construction site, and an extended Behavior Tree. At the end of this study, the results of the initial validation tests of autonomous earthwork operations using an OPERA-compatible backhoe ZX200 are presented.

Task Management System for Construction Machinery Using the Open Platform OPERA

We propose a novel on-line multi-UAV coverage path planning method for 3D reconstruction. UAVs play ho- mogeneous roles in most existing multi-UAV path planning methods, where their flexibility is not fully exploited. The fact that UAVs can explore with different missions and strategies at different heights simultaneously is not well considered. Therefore, we give UAVs different identities by presenting a global-local pattern, where global UAV locates the interesting regions while local UAV explores these regions in succession. Leveraging the mechanism of sensorimotor coordination, our strategy adjusts the paths of local UAV on-line according to the input from global UAV in order to gather more information and save time. In addition, our method is compatible with real-time SLAM systems. Simulation shows that our strategy takes less time than other strategies based on area-division, and indicates that it gathers more information for better reconstruction.

Sensorimotor Coordinated Multi-UAV Coverage Path Planning

It is essentially required to predict the ball's flight trajectory accurately and timely for a robotic table tennis ball bouncing task. Existing solutions, which can be categorized into model-based and learning-based groups, both exhibits unpleasant disadvantages. For example, they often require to identify many dynamic parameters accurately or to collect extensive labeled data, which are generally very difficult or costly to achieve in real world. In this paper, we proposed a light-wight and rapid trajectory prediction approach for online table tennis bouncing tasks based on a simplified model. In the proposed approach, the ball's flight poses are captured and estimated by a low-cost RGB-D camera. Then the ball's landing position is predicted in advance by using a fitted 3D parabola. Compared with existing solutions, our proposed approach is lightweight and easy to deploy. In experiments, 66 flight trajectories of the ball are collected to serve as benchmark. The prediction errors for all landing positions are all less than 20mm, in which most of them are less than 10mm. In addition, the prediction can be achieved 141.7ms in advance, which is fast enough for the robotic arm to plan and move itself to the predicted landing point.

A Light-Weight and Rapid Table Tennis Ball Trajectory Prediction Approaches towards Online Bouncing Task

There is an interest in and need for the use of semiautonomous robots in various fields, such as disaster response. Over the years, different techniques for semiautonomous control were developed, ranging from teleoperation guidance to different user interface designs for setting task constraints and goals interactively. Among those, affordance templates emerged as a recent method for users to efficiently provide robots with contextual information about object shapes, properties, and affordances. In many fields where direct teleoperation is common, affordance templates seem to be a promising candidate for improving performance and usability. However, despite the reports on the potential benefits of this technique in comparison to direct teleoperation, they are often qualitative or focus on tasks where teleoperation is particularly challenging. This can be a problem because task difficulty can influence different performance metrics and human factors, so results from studies that show large differences in task difficulty between interaction modes cannot be directly generalized to tasks with smaller or non-existent differences in difficulty. In this study, we aim to evaluate the effectiveness of affordance templates in the context of debris removal for disaster response, a task where direct teleoperation is a viable technique. We compared the two methods in a simulated setting through a user study involving 41 participants by measuring a) usability through questionnaires and b) performance on secondary tasks, an established measure of spare information processing capacity. The study results show that despite similar difficulty, users performed better on secondary tasks when using affordance template-based semiautonomy in this setting.

An Evaluation of Affordance Templates for Human-Robot Interaction

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware.

Low-Cost Teleoperation with Haptic Feedback through Vision-Based Tactile Sensors for Rigid and Soft Object Manipulation

Autonomous Guided Vehicles (AGVs) are becoming increasingly common in industrial environments to transport heavy equipment around warehouses. Within the idea of Industry 5.0, these AGVs are expected to work alongside humans in the same shared workspace. To enable smooth and trustworthy interaction between workers and AGVs, the AGVs must be able to model the workers' behavior and plan their trajectories around it. In this paper, we introduce a Finite Automaton Model (FAM) to model worker walking behavior in such a context. We conduct a human subject experiment using a Virtual Reality (VR) environment to collect data about worker trajectories to tune our model. We show that not only is our model more interpretable, but also outperforms machine learning models at classifying worker behavior with limited training data. Future research can use our model to modify AGV behavior to promote trustworthy human-AGV interaction.

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Identifying and Detecting Inadvertent Socially Inappropriate Movement of Avatar Robots

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