Robotic telepresence enables users to navigate and experience remote environments. However, effective navigation and situational awareness depend on users' prior knowledge of the environment, limiting the usefulness of these systems for exploring unfamiliar places. We explore how integrating location-aware LLM-based narrative capabilities into a mobile robot can support remote exploration. We developed a prototype system, called NarraGuide, that provides narrative guidance for users to explore and learn about a remote place through a dialogue-based interface. We deployed our prototype in a geology museum, where remote participants (n=20) used the robot to tour the museum. Our findings reveal how users perceived the robot's role, engaged in dialogue in the tour, and expressed preferences for bystander encountering. Our work demonstrates the potential of LLM-enabled robotic capabilities to deliver location-aware narrative guidance and enrich the experience of exploring remote environments.

People feel attached to places that are meaningful to them, which psychological research calls "place attachment." Place attachment is associated with self-identity, self-continuity, and psychological well-being. Even small cues, including videos, images, sounds, and scents, can facilitate feelings of connection and belonging to a place. Telepresence robots that allow people to see, hear, and interact with a remote place have the potential to establish and maintain a connection with places and support place attachment. In this paper, we explore the design space of robotic telepresence to promote place attachment, including how users might be guided in a remote place and whether they experience the environment individually or with others. We prototyped a telepresence robot that allows one or more remote users to visit a place and be guided by a local human guide or a conversational agent. Participants were 38 university alumni who visited their alma mater via the telepresence robot. Our findings uncovered four distinct user personas in the remote experience and highlighted the need for social participation to enhance place attachment. We generated design implications for future telepresence robot design to support people's connections with places of personal significance.

This paper studies the problem of federated training of a deep neural network (DNN) over a random access channel (RACH) such as in computer networks, wireless networks, and cellular systems. More precisely, a set of remote users participate in training a centralized DNN model using SGD under the coordination of a parameter server (PS). The local model updates are transmitted from the remote users to the PS over a RACH using a slotted ALOHA protocol. The PS collects the updates from the remote users, accumulates them, and sends central model updates to the users at regular time intervals. We refer to this setting as the RACH-FL setting. The RACH-FL setting crucially addresses the problem of jointly designing a (i) client selection and (ii) gradient compression strategy which addresses the communication constraints between the remote users and the PS when transmission occurs over a RACH. For the RACH-FL setting, we propose a policy, which we term the ''age-of-gradient'' (AoG) policy in which (i) gradient sparsification is performed using top-K sparsification, (ii) the error correction is performed using memory accumulation, and (iii) the slot transmission probability is obtained by comparing the current local memory magnitude minus the magnitude of the gradient update to a threshold. Intuitively, the AoG measure of ''freshness'' of the memory state is reminiscent of the concept of age-of-information (AoI) in the context of communication theory and provides a rather natural interpretation of this policy. Numerical simulations show the superior performance of the AoG policy as compared to other RACH-FL policies.

In this paper, we introduce Robi Butler, a novel household robotic system that enables multimodal interactions with remote users. Building on the advanced communication interfaces, Robi Butler allows users to monitor the robot's status, send text or voice instructions, and select target objects by hand pointing. At the core of our system is a high-level behavior module, powered by Large Language Models (LLMs), that interprets multimodal instructions to generate action plans. These plans are composed of a set of open vocabulary primitives supported by Vision Language Models (VLMs) that handle both text and pointing queries. The integration of the above components allows Robi Butler to ground remote multimodal instructions in the real-world home environment in a zero-shot manner. We demonstrate the effectiveness and efficiency of this system using a variety of daily household tasks that involve remote users giving multimodal instructions. Additionally, we conducted a user study to analyze how multimodal interactions affect efficiency and user experience during remote human-robot interaction and discuss the potential improvements.

This article presents Teledrive, a telepresence robotic system with embodied AI features that empowers an operator to navigate the telerobot in any unknown remote place with minimal human intervention. We conceive Teledrive in the context of democratizing remote care-giving for elderly citizens as well as for isolated patients, affected by contagious diseases. In particular, this paper focuses on the problem of navigating to a rough target area (like bedroom or kitchen) rather than pre-specified point destinations. This ushers in a unique AreaGoal based navigation feature, which has not been explored in depth in the contemporary solutions. Further, we describe an edge computing-based software system built on a WebRTC-based communication framework to realize the aforementioned scheme through an easy-to-use speech-based human-robot interaction. Moreover, to enhance the ease of operation for the remote caregiver, we incorporate a person following feature, whereby a robot follows a person on the move in its premises as directed by the operator. Moreover, the system presented is loosely coupled with specific robot hardware, unlike the existing solutions. We have evaluated the efficacy of the proposed system through baseline experiments, user study, and real-life deployment.

This paper introduces HoloBots, a mixed reality remote collaboration system that augments holographic telepresence with synchronized mobile robots. Beyond existing mixed reality telepresence, HoloBots lets remote users not only be visually and spatially present, but also physically engage with local users and their environment. HoloBots allows the users to touch, grasp, manipulate, and interact with the remote physical environment as if they were co-located in the same shared space. We achieve this by synchronizing holographic user motion (Hololens 2 and Azure Kinect) with tabletop mobile robots (Sony Toio). Beyond the existing physical telepresence, HoloBots contributes to an exploration of broader design space, such as object actuation, virtual hand physicalization, world-in-miniature exploration, shared tangible interfaces, embodied guidance, and haptic communication. We evaluate our system with twelve participants by comparing it with hologram-only and robot-only conditions. Both quantitative and qualitative results confirm that our system significantly enhances the level of co-presence and shared experience, compared to the other conditions.

In 2010, Microsoft's Kinect smart camera recognized you and logged you in to your Xbox. Now Microsoft is applying the same principles to meetings, announcing support for a future lineup of intelligent cameras that will zoom in and highlight you in hybrid meetings. Support for intelligent cameras in Microsoft Teams is just part of several announcements Microsoft made Thursday regarding the future of so-called hybrid work, where employees navigate between working at home and in the office. New features include the ability to tell coworkers whether you'll be working from home or in the office via Outlook, be it your weekly schedule or an RSVP for a specific meeting, and to stream video of yourself as you present a PowerPoint presentation. Intelligent meeting cameras represent the evolution of Microsoft's approach to hybrid work.

Getting an extra pair of arms is now as easy as putting on a backpack. Researchers at the University of Tokyo and Keio University created a telepresence robotic system called'Fusion' that has a head and two arms that are controlled remotely. The remote person not only sees what the wearer sees, but they can also control the robot's arms, or even use the robot to manipulate the wearer's arms. A telepresence robot is a remote-controlled device that typically moves around using a set of wheels. They've been adapted for a variety of uses, from communications tools for consumers or even for use by businesses.

A new "human Uber" could let you pay someone to live your life for you. Japanese researcher Jun Rekimoto has developed a special screen that can be strapped to a person's face and allow them to live on your behalf. By dressing up as you and having your face shown where theirs usually is, you'll be able to pay someone to go about your life instead. The technology is aimed at allowing someone – a "surrogate" – to live your life for you, wearing your clothes and behaving on your instruction. You, on the other hand, would be able to lounge at home, watching events through your laptop and using its camera to communicate with people your surrogate meets.

When developing algorithms for coordinating the behaviors of swarms of robots it is crucial that the algorithms are actually deployed and tested on real hardware platforms. Unfortunately, building and maintaining a swarm robotics testbed is a resource-intense proposition and, as a consequence, resources rather than ideas tend to be the bottleneck and swarm robotics research does not progress at the rate it could. The Robotarium sets out to remedy this problem by providing remote access to a large team of robots, where users can upload their code, run the experiments remotely, and get the scientific data back. This article describes the structure and architecture of the Robotarium as well as discusses what constitutes an effective, remotely accessible research platform. This paper won the IEEE Robotics & Automation Best Multi-Robot Systems Award at ICRA 2017.