While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract, high-level instructions to a 3D scene. High-Level instructions might not explicitly invoke semantic elements in the scene, and even the process of breaking a high-level task into a set of more concrete subtasks —a process called hierarchical task analysis— is environment-dependent. In this work, we propose ASHiTA, the first framework that generates a task hierarchy grounded to a 3D scene graph by breaking down high-level tasks into grounded subtasks. ASHiTA alternates LLM-assisted hierarchical task analysis —to generate the task breakdown— with task-driven 3D scene graph construction to generate a suitable representation of the environment. Our experiments show that ASHiTA performs significantly better than LLM baselines in breaking down high-level tasks into environment-dependent subtasks and is additionally able to achieve grounding performance comparable to state-of-the-art methods.

Advances in robotics and multisensory displays allow extending telepresence ambitions beyond only the feeling of being present at a remote location In this paper, we discuss what may lie beyond telepresence and how we can transport both the functional and social self of a user. We introduce the embodiment illusion and its potential contribution to task performance and list important cues to evoke this illusion, including synchronicity in multisensory information, a first-person visual perspective, and a human-like visual appearance and anatomy of the telepresence robot. We also introduce the concept of social presence and the important bidirectional social cues it needs, including eye contact, facial expression, posture, gestures, and social touch. For all these multisensory and social cues, we explain how they can be implemented in a telepresence system and describe our solution consisting of a closed control pod and a humanoid telepresence robot.

Virtual reality (VR) has created a new and rich medium for people to meet each other digitally. In VR, people can choose from a broad range of representations. In several cases, it is important to provide users with avatars that are a lifelike representation of themselves, to increase the user experience and effectiveness of communication. In this work, we propose a pipeline for generating a realistic and expressive avatar from a single reference image. The pipeline consists of a blendshape-based avatar combined with two deep learning improvements. The first improvement module runs offline and improves the texture map of the base avatar. The second module runs inference in real-time at the rendering stage and performs a style transfer to the avatar’s eyes. The deep learning modules effectively improve the visual representation of the avatar and show how AI techniques can be integrated with traditional animation methods to generate realistic human avatars for social VR.

This paper presents a proof-of-concept of a robotic teleoperation system, that provides the human operator a thermal sense in addition to the visual sense. With a sensor suite comprising a stereo camera, 360° camera and long-wave infra-red camera, our demonstrator pushes the boundaries of virtual-reality situational awareness by bringing not only 3D visual content but also a 360° thermal experience to the operator. The visual channel of our robotic teleoperation system is represented through a head-mounted-display and the thermal channel is displayed through directional heaters in the operator cockpit and a thermal glove. Initial tests showed that an operator successfully experienced a 360° remote environment, correctly distinguished between and interacted with hot and cold objects, and could notice the presence of nearby people outside her direct field-of-view, based on their emitted heat.

Recent years have seen a new uptake in immersive media and eXtended Reality (XR). And due to a global pandemic, computer-mediated communication over video conferencing tools became a new normal of everyday remote collaboration and virtual meetings. Social XR leverages XR technologies for remote communication and collaboration. But in order for XR to facilitate a high level of (social) presence and thus high-quality mediated social contact between users, we need high-quality 3D representation of users. One approach to providing detailed 3D user representations as new immersive media is to use point clouds or meshes, but these representation formats come with complexity on compression bitrate and processing time. In the example of virtual meetings, compression has to fulfill stringent requirements such as low latency and high quality. As the compression techniques for 3D immersive media steadily advance, it is important to be able to easily compare different compression techniques on their technical and visual merits in an easy way. The proposed demonstrator in this paper is a visualization tool that helps assessing the visual quality of a 3D representation employing various coding schemes. The complete end-to-end rendering/encoding chain can be easily assessed, allowing for subjective testing by showing the differences between the selected encoding parameters. The tool presented in this demo paper offers an improved and easy visual process for the comparison of encoders of immersive media.

We previously introduced a responsive joint attention system that uses multimodal information from users engaged in a spatial reasoning task with a robot and communicates joint attention via the robot's gaze behavior. An initial evaluation of our system with adults showed it to improve users' perceptions of the robot's social presence. To investigate the repeatability of our prior findings across settings and populations, here we conducted two further studies employing the same gaze system with the same robot and task but in different contexts: evaluation of the system with external observers and evaluation with children. The external observer study suggests that third-person perspectives over videos of gaze manipulations can be used either as a manipulation check before committing to costly real-time experiments or to further establish previous findings. However, the replication of our original adults study with children in school did not confirm the effectiveness of our gaze manipulation, suggesting that different interaction contexts can affect the generalizability of results in human-robot interaction gaze studies.

Social VR shall allow natural communication between users with high social presence, as if users are in the same room. One way to increase social presence is to add haptic interaction to allow, for example, users to give each other a ”high-five” or to pass documents among them. In this paper, we present our web-based VR communication framework with an added haptic component to simulate touch. The goal of this framework is to enhance the VR communication experience and the social cues exchange between users in VR. We describe our method for rendering haptic feedback within the web-based framework and evaluate the perceived quality of our system with a user survey (with 119 participants). Our proof-of-concept system was rated positively, with the haptic component offering an enhanced quality of the VR experience for 78% of the participants.

Joint attention has been shown to be not only crucial for human-human interaction but also human-robot interaction. Joint attention can help to make cooperation more efficient, support disambiguation in instances of uncertainty and make interactions appear more natural and familiar. In this paper, we present an autonomous gaze system that uses multimodal perception capabilities to model responsive joint attention mechanisms. We investigate the effects of our system on people's perception of a robot within a problem-solving task. Results from a user study suggest that responsive joint attention mechanisms evoke higher perceived feelings of social presence on scales that regard the direction of the robot's perception.