arXiv subjects

Zhao Han

Publications and source records attributed to Zhao Han.

15 recordsLinked to original sources

arXiv2025

Taiji: A DPU Memory Elasticity Solution for In-production Cloud Environments

The growth of cloud computing drives data centers toward higher density and efficiency. Data processing units (DPUs) enhance server network and storage performance but face challenges such as long hardware upgrade cycles and limited resources. To address these, we propose Taiji, a resource-elasticity architecture for DPUs. Combining hybrid virtualization with parallel memory swapping, Taiji switches the DPU's operating system (OS) into a guest OS and inserts a lightweight virtualization layer, making nearly all DPU memory swappable. It achieves memory overcommitment for the switched guest OS via high-performance memory elasticity, fully transparent to upper-layer applications, and supports hot-switch and hot-upgrade to meet in-production cloud requirements. Experiments show that Taiji expands DPU memory resources by over 50%, maintains virtualization overhead around 5%, and ensures 90% of swap-ins complete within 10 microseconds. Taiji delivers an efficient, reliable, low-overhead elasticity solution for DPUs and is deployed in large-scale production systems across more than 30,000 servers.

cs.OS↗

arXiv2025

Indicating Robot Vision Capabilities with Augmented Reality

Research indicates that humans can mistakenly assume that robots and humans have the same field of view, possessing an inaccurate mental model of robots. This misperception may lead to failures during human-robot collaboration tasks where robots might be asked to complete impossible tasks about out-of-view objects. The issue is more severe when robots do not have a chance to scan the scene to update their world model while focusing on assigned tasks. To help align humans' mental models of robots' vision capabilities, we propose four field-of-view indicators in augmented reality and conducted a human-subjects experiment (N=41) to evaluate them in a collaborative assembly task regarding accuracy, confidence, task efficiency, and workload. These indicators span a spectrum of positions: two at robot's eye and head space -- deepening eye socket and adding blocks to two sides of the eyes (i.e., egocentric), and two anchoring in the robot's task space -- adding extended blocks from the sides of eyes to the table and placing blocks directly on the tables (i.e., allocentric). Results showed that, when placed directly in the task space, the allocentric indicator yields the highest accuracy, although with a delay in interpreting the robot's field of view. When placed at the robot's eyes, the egocentric indicator of deeper eye sockets, possible for physical alteration, also increased accuracy. In all indicators, participants' confidence was high while cognitive load remained low. Finally, we contribute six guidelines for practitioners to apply our augmented reality indicators or physical alterations to align humans' mental models with robots' vision capabilities.

cs.RO↗

arXiv2024

Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community

The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces and not just with computers, but with all machines including robots. In this paper, we chronicle the recent history of this growing field of spoken dialogue with robots and offer the community three proposals, the first focused on education, the second on benchmarks, and the third on the modeling of language when it comes to spoken interaction with robots. The three proposals should act as white papers for any researcher to take and build upon.

cs.CL↗

arXiv2022

Mixed-Reality Robot Behavior Replay: A System Implementation

As robots become increasingly complex, they must explain their behaviors to gain trust and acceptance. However, it may be difficult through verbal explanation alone to fully convey information about past behavior, especially regarding objects no longer present due to robots' or humans' actions. Humans often try to physically mimic past movements to accompany verbal explanations. Inspired by this human-human interaction, we describe the technical implementation of a system for past behavior replay for robots in this tool paper. Specifically, we used Behavior Trees to encode and separate robot behaviors, and schemaless MongoDB to structurally store and query the underlying sensor data and joint control messages for future replay. Our approach generalizes to different types of replays, including both manipulation and navigation replay, and visual (i.e., augmented reality (AR)) and auditory replay. Additionally, we briefly summarize a user study to further provide empirical evidence of its effectiveness and efficiency. Sample code and instructions are available on GitHub at https://github.com/umhan35/robot-behavior-replay.

cs.RO↗

arXiv2022

Proceedings of the AI-HRI Symposium at AAAI-FSS 2022

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration on AI theory and methods aimed at HRI since 2014. This year, after a review of the achievements of the AI-HRI community over the last decade in 2021, we are focusing on a visionary theme: exploring the future of AI-HRI. Accordingly, we added a Blue Sky Ideas track to foster a forward-thinking discussion on future research at the intersection of AI and HRI. As always, we appreciate all contributions related to any topic on AI/HRI and welcome new researchers who wish to take part in this growing community. With the success of past symposia, AI-HRI impacts a variety of communities and problems, and has pioneered the discussions in recent trends and interests. This year's AI-HRI Fall Symposium aims to bring together researchers and practitioners from around the globe, representing a number of university, government, and industry laboratories. In doing so, we hope to accelerate research in the field, support technology transition and user adoption, and determine future directions for our group and our research.

cs.AI↗

arXiv2022

Causal Robot Communication Inspired by Observational Learning Insights

Autonomous robots must communicate about their decisions to gain trust and acceptance. When doing so, robots must determine which actions are causal, i.e., which directly give rise to the desired outcome, so that these actions can be included in explanations. In behavior learning in psychology, this sort of reasoning during an action sequence has been studied extensively in the context of imitation learning. And yet, these techniques and empirical insights are rarely applied to human-robot interaction (HRI). In this work, we discuss the relevance of behavior learning insights for robot intent communication, and present the first application of these insights for a robot to efficiently communicate its intent by selectively explaining the causal actions in an action sequence.

cs.RO↗

arXiv2022

Towards Formalizing HRI Data Collection Processes

Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies. However, other parts of the community, e.g., the technical advances community, also need to do human-subjects studies to collect data to train their models, in ways that require user studies but without a strict experimental design. The design of such data collection is an underexplored area worthy of more attention. In this work, we contribute a clearly defined process to collect data with three steps for machine learning modeling purposes, grounded in recent literature, and detail an use of this process to facilitate the collection of a corpus of referring expressions. Specifically, we discuss our data collection goal and how we worked to encourage well-covered and abundant participant responses, through our design of the task environment, the task itself, and the study procedure. We hope this work would lead to more data collection formalism efforts in the HRI community and a fruitful discussion during the workshop.

cs.RO↗

arXiv2021

Projecting Robot Navigation Paths: Hardware and Software for Projected AR

For mobile robots, mobile manipulators, and autonomous vehicles to safely navigate around populous places such as streets and warehouses, human observers must be able to understand their navigation intent. One way to enable such understanding is by visualizing this intent through projections onto the surrounding environment. But despite the demonstrated effectiveness of such projections, no open codebase with an integrated hardware setup exists. In this work, we detail the empirical evidence for the effectiveness of such directional projections, and share a robot-agnostic implementation of such projections, coded in C++ using the widely-used Robot Operating System (ROS) and rviz. Additionally, we demonstrate a hardware configuration for deploying this software, using a Fetch robot, and briefly summarize a full-scale user study that motivates this configuration. The code, configuration files (roslaunch and rviz files), and documentation are freely available on GitHub at https://github.com/umhan35/arrow_projection.

cs.RO↗

arXiv2021

AI-HRI 2021 Proceedings

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. During that time, these symposia provided a fertile ground for numerous collaborations and pioneered many discussions revolving trust in HRI, XAI for HRI, service robots, interactive learning, and more. This year, we aim to review the achievements of the AI-HRI community in the last decade, identify the challenges facing ahead, and welcome new researchers who wish to take part in this growing community. Taking this wide perspective, this year there will be no single theme to lead the symposium and we encourage AI-HRI submissions from across disciplines and research interests. Moreover, with the rising interest in AR and VR as part of an interaction and following the difficulties in running physical experiments during the pandemic, this year we specifically encourage researchers to submit works that do not include a physical robot in their evaluation, but promote HRI research in general. In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI. Over the course of the two-day meeting, we will host a collaborative forum for discussion of current efforts in AI-HRI, with additional talks focused on the topics of ethics in HRI and ubiquitous HRI.

cs.RO↗

arXiv2021

Investigation of Multiple Resource Theory Design Principles on Robot Teleoperation and Workload Management

Robot interfaces often only use the visual channel. Inspired by Wickens' Multiple Resource Theory, we investigated if the addition of audio elements would reduce cognitive workload and improve performance. Specifically, we designed a search and threat-defusal task (primary) with a memory test task (secondary). Eleven participants - predominantly first responders - were recruited to control a robot to clear all threats in a combination of four conditions of primary and secondary tasks in visual and auditory channels. We did not find any statistically significant differences in performance or workload across subjects, making it questionable that Multiple Resource Theory could shorten longer-term task completion time and reduce workload. Our results suggest that considering individual differences for splitting interface modalities across multiple channels requires further investigation.

cs.RO↗

arXiv2020

Proceedings of the AI-HRI Symposium at AAAI-FSS 2020

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. In that time, the related topic of trust in robotics has been rapidly growing, with major research efforts at universities and laboratories across the world. Indeed, many of the past participants in AI-HRI have been or are now involved with research into trust in HRI. While trust has no consensus definition, it is regularly associated with predictability, reliability, inciting confidence, and meeting expectations. Furthermore, it is generally believed that trust is crucial for adoption of both AI and robotics, particularly when transitioning technologies from the lab to industrial, social, and consumer applications. However, how does trust apply to the specific situations we encounter in the AI-HRI sphere? Is the notion of trust in AI the same as that in HRI? We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium. Over the course of the two-day meeting, we propose to create a collaborative forum for discussion of current efforts in trust for AI-HRI, with a sub-session focused on the related topic of explainable AI (XAI) for HRI.

cs.RO↗

arXiv2020

Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability

Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states such as perception results and action intent. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient, eliminating mental inference about the robot's intention. However, there is a lack of tools for projection mapping in robotics, compared to established motion planning libraries (e.g., MoveIt). In this paper, we detail the implementation of projection mapping to enable researchers and practitioners to push the boundaries for better interaction between robots and humans. We also provide practical documentation and code for a sample manipulation projection mapping on GitHub: https://github.com/uml-robotics/projection_mapping.

cs.RO↗

arXiv2020

Reasons People Want Explanations After Unrecoverable Pre-Handover Failures

Most research on human-robot handovers focuses on the development of comfortable and efficient HRI; few have studied handover failures. If a failure occurs in the beginning of the interaction, it prevents the whole handover process and destroys trust. Here we analyze the underlying reasons why people want explanations in a handover scenario where a robot cannot possess the object. Results suggest that participants set expectations on their request and that a robot should provide explanations rather than non-verbal cues after failing. Participants also expect that their handover request can be done by a robot, and, if not, would like to be able to fix the robot or change the request based on the provided explanations.

cs.RO↗

arXiv2020

Towards Mobile Multi-Task Manipulation in a Confined and Integrated Environment with Irregular Objects

The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrated task sets, irregular objects, confined space, and machining, introducing new challenges in the mobile manipulation domain. Here we describe our efforts to address these challenges by demonstrating the assembly of a kit of mechanical parts in a caddy. In addition to implementation details, we examine the issues in this task set extensively, and we discuss our software architecture in the hope of providing a base for other researchers. To evaluate performance and consistency, we conducted 20 full runs, then examined failure cases with possible solutions. We conclude by identifying future research directions to address the open challenges.

cs.RO↗

arXiv2019

Towards A Robot Explanation System: A Survey and Our Approach to State Summarization, Storage and Querying, and Human Interface

As robot systems become more ubiquitous, developing understandable robot systems becomes increasingly important in order to build trust. In this paper, we present an approach to developing a holistic robot explanation system, which consists of three interconnected components: state summarization, storage and querying, and human interface. To find trends towards and gaps in the development of such an integrated system, a literature review was performed and categorized around those three components, with a focus on robotics applications. After the review of each component, we discuss our proposed approach for robot explanation. Finally, we summarize the system as a whole and review its functionality.

cs.RO↗