| 1. |
- Avramova, Vanya, et al.
(author)
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A virtual poster presenter using mixed reality
- 2017
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In: 17th International Conference on Intelligent Virtual Agents, IVA 2017. - Cham : Springer. - 9783319674001 ; , s. 25-28
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Conference paper (peer-reviewed)abstract
- In this demo, we will showcase a platform we are currently developing for experimenting with situated interaction using mixed reality. The user will wear a Microsoft HoloLens and be able to interact with a virtual character presenting a poster. We argue that a poster presentation scenario is a good test bed for studying phenomena such as multi-party interaction, speaker role, engagement and disengagement, information delivery, and user attention monitoring.
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| 2. |
- Gao, Alex Yuan, et al.
(author)
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Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
- 2019
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In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN. - : IEEE. - 9781728126227
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Conference paper (peer-reviewed)abstract
- Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-the-art model.
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| 3. |
- Li, Chengjie, et al.
(author)
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Effects of Posture and Embodiment on Social Distance in Human-Agent Interaction in Mixed Reality
- 2018
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In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. - New York, NY, USA : ACM Digital Library. - 9781450360135 ; , s. 191-196
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Conference paper (peer-reviewed)abstract
- Mixed reality offers new potentials for social interaction experiences with virtual agents. In addition, it can be used to experiment with the design of physical robots. However, while previous studies have investigated comfortable social distances between humans and artificial agents in real and virtual environments, there is little data with regards to mixed reality environments. In this paper, we conducted an experiment in which participants were asked to walk up to an agent to ask a question, in order to investigate the social distances maintained, as well as the subject's experience of the interaction. We manipulated both the embodiment of the agent (robot vs. human and virtual vs. physical) as well as closed vs. open posture of the agent. The virtual agent was displayed using a mixed reality headset. Our experiment involved 35 participants in a within-subject design. We show that, in the context of social interactions, mixed reality fares well against physical environments, and robots fare well against humans, barring a few technical challenges.
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| 4. |
- Peters, Christopher, et al.
(author)
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Investigating Social Distances between Humans, Virtual Humans and Virtual Robots in Mixed Reality
- 2018
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In: Proceedings of 17th International Conference on Autonomous Agents and MultiAgent Systems. ; , s. 2247-2249
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Conference paper (peer-reviewed)abstract
- Mixed reality environments offer new potentials for the design of compelling social interaction experiences with virtual characters. In this paper, we summarise initial experiments we are conducting in which we measure comfortable social distances between humans, virtual humans and virtual robots in mixed reality environments. We consider a scenario in which participants walk within a comfortable distance of a virtual character that has its appearance varied between a male and female human, and a standard- and human-height virtual Pepper robot. Our studies in mixed reality thus far indicate that humans adopt social zones with artificial agents that are similar in manner to human-human social interactions and interactions in virtual reality.
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| 5. |
- Peters, Christopher, et al.
(author)
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Towards the use of mixed reality for hri design via virtual robots
- 2018
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In: HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot InteractionMarch 2020.
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Conference paper (peer-reviewed)abstract
- Mixed reality, which seeks to better merge virtual objects and theirinteractions with the real environment, offers numerous potentialsfor the improved design of robots and our interactions with them. Inthis paper, we present our ongoing work towards the developmentof a mixed reality platform for designing social interactions withrobots through the use of virtual robots. We present a summaryour work thus far on the use of the platform for investigatingproxemics between humans and virtual robots, and also highlightfuture research directions. These include the consideration of moresophisticated interactions involving verbal behaviours, interactionwith small formations of virtual robots, better integration of virtualobjects into real environments and experiments comparing the realsystems with their virtual counterparts.
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| 6. |
- Ravichandran, Naresh Balaji, et al.
(author)
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Pedestrian simulation as multi-objective reinforcement learning
- 2018
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In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018. - New York, NY, USA : ACM. - 9781450360135 ; , s. 307-312
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Conference paper (peer-reviewed)abstract
- Modelling and simulation of pedestrian crowds require agents to reach pre-determined goals and avoid collisions with static obstacles and dynamic pedestrians, while maintaining natural gait behaviour. We model pedestrians as autonomous, learning, and reactive agents employing Reinforcement Learning (RL). Typical RL-based agent simulations suffer poor generalization due to handcrafted reward function to ensure realistic behaviour. In this work, we model pedestrians in a modular framework integrating navigation and collision-avoidance tasks as separate modules. Each such module consists of independent state-spaces and rewards, but with shared action-spaces. Empirical results suggest that such modular framework learning models can show satisfactory performance without tuning parameters, and we compare it with the state-of-art crowd simulation methods.
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| 7. |
- Saikia, Himangshu, et al.
(author)
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Criticality-based collision avoidance prioritization for crowd navigation
- 2019
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In: HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450369220 ; , s. 153-161
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Conference paper (peer-reviewed)abstract
- Goal directed agent navigation in crowd simulations involves a complex decision making process. An agent must avoid all collisions with static or dynamic obstacles (such as other agents) and keep a trajectory faithful to its target at the same time. This seemingly global optimization problem can be broken down into smaller local optimization problems by looking at a concept of criticality. Our method resolves critical agents - agents that are likely to come within collision range of each other - in order of priority using a Particle Swarm Optimization scheme. The resolution involves altering the velocities of agents to avoid criticality. Results from our method show that the navigation problem can be solved in several important test cases with minimal number of collisions and minimal deviation to the target direction. We prove the efficiency and correctness of our method by comparing it to four other well-known algorithms, and performing evaluations on them based on various quality measures.
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| 8. |
- Saikia, Himangshu, et al.
(author)
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Priority driven Local Optimization for Crowd Simulation
- 2019
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In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. - : Association for Computing Machinery (ACM). - 9781450363099 ; , s. 2180-2182
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Conference paper (peer-reviewed)abstract
- We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.
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| 9. |
- Yang, Fangkai, et al.
(author)
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A dataset of human and robot approach behaviors into small free-standing conversational groups
- 2021
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 16:2
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Journal article (peer-reviewed)abstract
- The analysis and simulation of the interactions that occur in group situations is important when humans and artificial agents, physical or virtual, must coordinate when inhabiting similar spaces or even collaborate, as in the case of human-robot teams. Artificial systems should adapt to the natural interfaces of humans rather than the other way around. Such systems should be sensitive to human behaviors, which are often social in nature, and account for human capabilities when planning their own behaviors. A limiting factor relates to our understanding of how humans behave with respect to each other and with artificial embodiments, such as robots. To this end, we present CongreG8 (pronounced 'con-gregate'), a novel dataset containing the full-body motions of free-standing conversational groups of three humans and a newcomer that approaches the groups with the intent of joining them. The aim has been to collect an accurate and detailed set of positioning, orienting and full-body behaviors when a newcomer approaches and joins a small group. The dataset contains trials from human and robot newcomers. Additionally, it includes questionnaires about the personality of participants (BFI-10), their perception of robots (Godspeed), and custom human/robot interaction questions. An overview and analysis of the dataset is also provided, which suggests that human groups are more likely to alter their configuration to accommodate a human newcomer than a robot newcomer. We conclude by providing three use cases that the dataset has already been applied to in the domains of behavior detection and generation in real and virtual environments.
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| 10. |
- Yang, Fangkai, et al.
(author)
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App-LSTM : Data-driven generation of socially acceptable trajectories for approaching small groups of agents
- 2019
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In: HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450369220 ; , s. 144-152
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Conference paper (peer-reviewed)abstract
- While many works involving human-agent interactions have focused on individuals or crowds, modelling interactions on the group scale has not been considered in depth. Simulation of interactions with groups of agents is vital in many applications, enabling more comprehensive and realistic behavior encompassing all possibilities between crowd and individual levels. In this paper, we propose a novel neural network App-LSTM to generate the approach trajectory of an agent towards a small free-standing conversational group of agents. The App-LSTM model is trained on a dataset of approach behaviors towards the group. Since current publicly available datasets for these encounters are limited, we develop a social-aware navigation method as a basis for creating a semi-synthetic dataset composed of a mixture of real and simulated data representing safe and socially-acceptable approach trajectories. Via a group interaction module, App-LSTM then captures the position and orientation features of the group and refines the current state of the approaching agent iteratively to better focus on the current intention of group members. We show our App-LSTM outperforms baseline methods in generating approaching group trajectories.
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