Demonstrative learning in virtual reality (VR) is a pivotal learning strategy for knowledge transfer for embodied agents. While existing studies have extensively explored agents’ knowledge transfer through self-demonstrative learning (SDL) or teacher-demonstrative learning (TDL), there has been limited focus on a system that integrates both paradigms. This article proposes a comprehensive system that combines the SDL paradigm with the TDL paradigm in VR from a top-down perspective. The system involves using directed probabilistic graphs (DPGs) for knowledge representation, constructing detectors and actuators based on object fluents and atomic actions, representing knowledge acquired from both learning paradigms on the DPGs, and incorporating knowledge integration and visualization. Through system evaluation, we show the advantages of integrating two demonstrative learning paradigms, including increased learning efficiency, mitigating demonstrator’s deficiencies, and more logical task execution. The study also reveals that a dynamically decreasing fusion factor with the learning progresses, performs well across different correct percentages of teacher demonstrations. Additionally, we show a more decentralized demonstration in the middle of the agent’s learning progress maximizing learning efficiency when demonstrating only a few atomic actions. Finally, we assess the system’s generalizability and delineate its boundaries. With the ongoing development and the increasing availability of human data in VR, we anticipate that our system can be applied to future scenarios of efficient human-agent knowledge transfer in human-agent symbiosis.

We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concepts, which is exclusively depicted by images from the positive set. Our benchmark inherits the few-shot concept induction of the original BPs while adding the two novel layers of challenge: 1) open-world free-form concepts, as the visual concepts in Bongard-OpenWorld are unique compositions of terms from an open vocabulary, ranging from object categories to abstract visual attributes and commonsense factual knowledge; 2) real-world images, as opposed to the synthetic diagrams used by many counterparts. In our exploration, Bongard-OpenWorld already imposes a significant challenge to current few-shot reasoning algorithms. We further investigate to which extent the recently introduced Large Language Models (LLMs) and Vision-Language Models (VLMs) can solve our task, by directly probing VLMs, and combining VLMs and LLMs in an interactive reasoning scheme. We even conceived a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems. However, none of these approaches manage to close the human-machine gap, as the best learner achieves 64% accuracy while human participants easily reach 91%. We hope Bongard-OpenWorld can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities.

Artificial intelligence (AI) has revolutionized human cognitive abilities and facilitated the development of new AI entities capable of interacting with humans in both physical and virtual environments. Despite the existence of virtual reality, mixed reality, and augmented reality for many years, integrating these technical fields remains a formidable challenge due to their disparate application directions. The advent of AI agents, capable of autonomous perception and action, further compounds this issue by exposing the limitations of traditional human-centered research approaches. It is imperative to establish a comprehensive framework that accommodates the dual perceptual centers of humans and AI agents in both physical and virtual worlds. In this paper, we introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. This framework enables researchers to better comprehend how AI agents can collaborate with humans and how distinct technical pathways of physical-virtual integration can be consolidated from a broader perspective. We then delve into the coexistence of humans and AI, demonstrating a prototype system that exemplifies the operation of symmetrical reality systems for specific tasks, such as pouring water. Finally, we propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality. This paper aims to offer beneficial perspectives and guidance for researchers and practitioners in different fields, thus contributing to the ongoing research about human-AI coexistence in both physical and virtual environments.

Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physical and virtual environments. While technological advances in personal digital devices, such as personal computers and virtual reality devices, can provide immersive experiences, they can also disrupt users’ awareness of their surroundings and enhance the frustration caused by disturbances. In this paper, we propose a joint observation strategy for artificial agents to support users across virtual and physical environments. We introduce a prototype system, neighbor-environment observer (NEO), that utilizes non-invasive sensors to assist users in dealing with disruptions to their immersive experience. System experiments evaluate NEO from different perspectives and demonstrate the effectiveness of the joint observation strategy. A user study is conducted to evaluate its usability. The results show that NEO could lessen users’ workload with the learned user preference. We suggest that the proposed strategy can be applied to various smart home scenarios.

National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), China Retrieving out-of-reach objects is a crucial task in virtual reality (VR). One of the most commonly used approaches for this task is the gesture-based approach, which allows for bare-hand, eyes-free, and direct retrieval. However, previous work has primarily focused on assigned gesture design, neglecting the context. This can make it challenging to accurately retrieve an object from a large number of objects due to the one-to-one mapping metaphor, limitations of finger poses, and memory burdens. There is a general consensus that objects and contexts are related, which suggests that the object expected to be retrieved is related to the context, including the scene and the objects with which users interact. As such, we propose a commonsense knowledge-driven joint reasoning approach for object retrieval, where human grasping gestures and context are modeled using an And-Or graph (AOG). This approach enables users to accurately retrieve objects from a large number of candidate objects by using natural grasping gestures based on their experience of grasping physical objects. Experimental results demonstrate that our proposed approach improves retrieval accuracy. We also propose an object retrieval system based on the proposed approach. Two user studies show that our system enables efficient object retrieval in virtual environments (VEs).

We study the hierarchical knowledge transfer problem using a cloth-folding task, wherein the agent is first given a set of human demonstrations in the virtual world using an Oculus Headset, and later transferred and validated on a physical Baxter robot. We argue that such an intricate robot task transfer across different embodiments is only realizable if an abstract and hierarchical knowledge representation is formed to facilitate the process, in contrast to prior literature of sim2real in a reinforcement learning setting. Specifically, the knowledge in both the virtual and physical worlds are measured by information entropy built on top of a graph-based representation, so that the problem of task transfer becomes the minimization of the relative entropy between the two worlds. An And-Or-Graph (AOG) is introduced to represent the knowledge, induced from the human demonstrations performed across six virtual scenarios inside the Virtual Reality (VR). During the transfer, the success of a physical Baxter robot platform across all six tasks demonstrates the efficacy of the graph-based hierarchical knowledge representation.

To investigate the effects of visual discomfort caused by long-term immersing in virtual environments (VEs), we conducted a comparative study to evaluate users’ visual discomfort in an eight-hour working rhythm and compared the differences between the VEs and the physical environments. Twenty-seven participants performed four different visual tasks with a head-mounted display (HMD) for the VE condition and with a monitor for the physical condition. Their subjective visual discomfort and objective oculomotor indicators were measured to evaluate their visual performances. The results show that the subjective visual fatigue symptoms, the objective pupil size, and the relative accommodation response vary across time for the two conditions, in which VEs affects visual fatigue the most compared to the physical environments. The results also show that pupil size is negatively related to subjective visual fatigue, and the long-term work based on displays only influences the maximum accommodation response of participants. This work is a supplement to the necessary but insufficient-researched field of visual fatigue in long-term immersing in VEs, which should be valuable to researchers involved in the evaluation of visual fatigue using HMDs.

This paper presents a design that jointly provides hand pose sensing, hand localization, and haptic feedback to facilitate real-time stable grasps in Virtual Reality (VR). The design is based on an easy-to-replicate glove-based system that can reliably perform (i) a high-fidelity hand pose sensing in real time through a network of 15 IMUs, and (ii) the hand localization using a Vive Tracker. The supported physics-based simulation in VR is capable of detecting collisions and contact points for virtual object manipulation, which drives the collision event to trigger the physical vibration motors on the glove to signal the user, providing a better realism inside virtual environments. A caging-based approach using collision geometry is integrated to determine whether a grasp is stable. In the experiment, we showcase successful grasps of virtual objects with large geometry variations. Comparing to the popular LeapMotion sensor, we demonstrate the proposed glove-based design yields a higher success rate in various tasks in VR. We hope such a glove-based system can simplify the data collection of human manipulations with VR.

Human-computer interaction (HCI) plays an important role in the near-field mixed reality, in which the hand-based interaction is one of the most widely-used interaction modes, especially in the applications based on optical see-through head-mounted displays (OST-HMDs). In this paper, such interaction modes as gesture-based interaction (GBI) and physics-based interaction (PBI) are developed to construct a mixed reality system to evaluate the advantages and disadvantages of different interaction modes. The ultimate goal is to find an efficient hybrid paradigm for mixed reality applications based on OST-HMDs to deal with the situations that a single interaction mode cannot handle. The results of the experiment, which compares GBI and PBI, show that PBI leads to a better performance of users regarding their work efficiency in the proposed two tasks. Some statistical tests, including T-test and one-way ANOVA, have also been adopted to prove that the difference regarding the efficiency between different interaction modes is significant. Experiments for combining both interaction modes are put forward in order to seek a good experience for manipulation, which proves that the partially-overlapping style would help to improve work efficiency for manipulation tasks. The experimental results of the proposed two hand-based interaction modes and their hybrid forms can provide some practical suggestions for the development of mixed reality systems based on OST-HMDs.

In a mixed reality (MR) environment that combines the physical objects with the virtual environments, users' feelings are immersed in the virtual world, while their bodies remain in the physical world. Compared to the purely physical environments, such characteristic has led to some special needs for users' long-term immersion. However, the deficiency needs that we have to face for long-term immersion still need further research. In this paper, we apply the theory of Maslow's Hierarchy of Needs (MHN) to guide the design of MR systems for long-term immersion. Taking the normal biological rhythm of human beings as the basic unit (24 hours), we propose the fundamental needs for long-term immersion in VEs through combining the theory of MHN with the special needs of virtual reality (VR). In order to verify whether those needs can satisfy users' long-term immersion, we design an MR office system for basic operations based on the theory of MHN. A long-term exposure experiment (duration of 8 hours) is designed to evaluate those needs by comparing the results with a physical work environment after a short-term preliminary study. The physiological and psychological effects are tested in both two environments and the deficiency needs for short-term immersion and long-term immersion are also compared. The results showed that the design based on the theory of MHN can support users' long-term immersion, which means that it can be a guideline for long-term use of MR systems.

Long-term exposure to VR will become more and more important, but what we need for long term immersion to meet users fundamental needs is still under-researched. In this paper, we apply the theory of Maslows Hierarchy of Needs to guide the design of VR for longterm immersion based on the normal biological rhythm of human beings (24 hours). An office environment is designed to verify those needs. The efficiency, the physical and the psychological effects of this VR office system are tested. The results show that the VR office environment is as comfortable as the physical environment at short-term immersion and it can support users basic immersion. It means that the Maslows Hierarchy of Needs can be a guideline for long-term immersion.

In this paper, we review the background of physical reality, virtual reality, and some traditional mixed forms of them. Based on the current knowledge, we propose a new unified concept called symmetrical reality to describe the physical and virtual world in a unified perspective. Under the framework of symmetrical reality, the traditional virtual reality, augmented reality, inverse virtual reality, and inverse augmented reality can be interpreted using a unified presentation. We analyze the characteristics of symmetrical reality from two different observation locations (i.e., from the physical world and from the virtual world), where all other forms of physical and virtual reality can be treated as special cases of symmetrical reality.

Since artificial intelligence has been integrated into virtual reality, a new branch of virtual reality, which is called inverse virtual reality (IVR), is created. A typical IVR system contains both the intelligence-driven virtual reality and the physical reality, thus constructing an intelligence-driven mutually mirrored world. We propose the concept of IVR, and describe the details about the definition, structure and implementation of a typical IVR system. The parallel living environment is proposed as a typical application of IVR, which reveals that IVR has a significant potential to extend the human living environment.

Hand-based interaction is one of the most widely-used interaction modes in the applications based on optical see-through head-mounted displays (OST-HMDs). In this paper, such interaction modes as gesture-based interaction (GBI) and physics-based interaction (PBI) are developed to construct a mixed reality system to evaluate the advantages and disadvantages of different interaction modes for near-field mixed reality. The experimental results show that PBI leads to a better performance of users regarding their work efficiency in the proposed tasks. The statistical analysis of T-test has been adopted to prove that the difference of efficiency between different interaction modes is significant.

Calibration accuracy is one of the most important factors to affect the user experience in mixed reality applications. For a typical mixed reality system built with the optical see-through head-mounted display (OST-HMD), a key problem is how to guarantee the accuracy of hand-eye coordination by decreasing the instability of the eye and the HMD in long-term use. In this paper, we propose a real-time latent active correction (LAC) algorithm to decrease hand-eye calibration errors accumulated over time. Experimental results show that we can successfully use the LAC algorithm to physics-inspired virtual input methods.

The most commonly used single point active alignment method (SPAAM) is based on a static pinhole camera model, in which it is assumed that both the eye and the HMD are fixed. This leads to a limitation for calibration precision. In this work, we propose a dynamic pinhole camera model according to the fact that the human eye would experience an obvious displacement over the whole calibration process. Based on such a camera model, we propose a new calibration data acquisition method called the region-induced data enhancement (RIDE) to revise the calibration data. The experimental results prove that the proposed dynamic model performs better than the traditional static model in actual calibration.