United States

We propose a framework for solving the mult-robot informating gathering problem leveraging a Gaussian Mixture Model (GMM) as prior knowledge for modeling informative regions in the target area. Taking samples from the GMM, informative robot paths are then computed that optimize the travel costs subject to energy budgets. Decomposing these paths into waypoints, robot trajectories are planned while respecting kinematic constraints and subsequently replanned online to
avoid collisions among themselves. The GMM model is incrementally updated by incorporating the gathered information. We demonstrate that our framework can achieve a significant amount of information gain with the optimal travel distance.

IEEE RO-MAN 2021

Education for New Human–Robot Coexistence: Countereffect and “Paragmatism” for the Weak Robot

resource constraints

information gathering

multi-robot path and trajectory planning

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

technical paper

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

Socially assistive robots and body weight support devices can be used to provide infants with opportunities to engage in various motor actions. Using methodological tools from social networks, we identify and describe the complex nature of an infant’s motor actions displayed during their longitudinal interaction with both types of technology in a robot-assisted early learning environment. These tools may inform the development of such learning environments that can be applied at this critical life period in early intervention.

Network Analysis of an Infant's Motor Actions Performed in a Robot-Assisted Learning Environment

In this paper, a teleoperated robot prototype was designed and tested to allow law enforcement officers to remotely and transparently communicate and interact with others. The robot featured near face-to-face interactivity and accuracy across multiple verbal and non-verbal modes using screens, microphones, and speakers. In cooperation with multiple law enforcement agencies, results are presented on this dynamic and integrative teleoperated communicative robot platform in terms of attitudes towards robots, trust in robot operation, and trust in human-robot-human interaction and communication.

Analyzing Human-Robot Trust in Police Work Using a Teleoperated Communicative Robot

Human-to-robot handovers of unknown containers such as drinking glasses with liquids can be unsafe for the human and the robot. We propose a real-to-simulation framework to develop human-to-robot handovers safely with estimations of the human hands and containers from videos of humans manipulating the containers. We also propose a method for estimating a safe region on the container for grasping and quantify the safeness of the human and object in simulation using noisy estimates from a range of perceptual algorithms.

Social Drone Sharing to Increase the UAV Patrolling Autonomy in Emergency Scenarios.

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our experiments demonstrate that this approach is generalizable to multilingual, highly varied datasets.

Neural Variational Learning for Grounded Language Acquisition

Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics. Challenges remain in these fields for generating high-fidelity samples and robustly reconstructing motion from imperfect input data, due to e.g. missed marker detection. In this paper, we propose a probabilistic generative model to synthesize and reconstruct long horizon motion sequences conditioned on past information and control signals, such as the path along which an individual is moving. Our method adapts the existing work MoGlow by introducing a new graph-based model. The model leverages the spatial-temporal graph convolutional network (ST-GCN) to effectively capture the spatial structure and temporal correlation of skeletal motion data at multiple scales. We evaluate the models on a mixture of motion capture datasets of human locomotion with foot-step and bone-length analysis. The results demonstrate the advantages of our model in reconstructing missing markers and achieving comparable results on generating realistic future poses. When the inputs are imperfect, our model shows improvements in the robustness of generation.

Defining Fairness in Human-Robot Teams

We propose a reinforcement learning model for the physical human–robot interaction. Our model estimates the opponent’s changing policy using variational autoencoder and expresses the agent’s policy in a common latent space. We verified the performance of the model using two novel physical and competitive environments, push-hand game and air-hockey in the simulator. The latent space worked properly as policy information of the agent’s own policy and that of the opponent.

Competitive Physical Interaction by Reinforcement Learning Agents Using Intention Estimation

The use of robotics in assisting humans and performingseveral different tasks is changing our lives. Unfortunately,for the execution of each new task, a time-consuming re-programming of the robot is necessary. This approach limitsthe adaptability of robots to new conditions. Ideally, robotsshould quickly adapt to unknown situations and learn directlyfrom experience or human demonstrations. What if even achild could teach a robot to drive autonomously?

Integrating an Observer in Interactive Reinforcement Learning to Learn Legible Trajectories

When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand. We develop a deep interpretable modular neural framework for modeling the intentions of other observed entities. We demonstrate the efficacy of our approach with experiments on data from human participants on a search and rescue task in Minecraft, and show that incorporating interpretability can significantly increase predictive performance under the right conditions.

Performing Human-Robot Interaction User Studies in Virtual Reality

We propose a force perturbation method to identify the human arm endpoint impedance behaviour in a dynamic task without specific requirements on force/position trajectories nor additional sensors placed on the human.
The method is illustrated through an experimental study on a cyclic benchmark interaction task, that offers sufficient variability to discard the use of previous patterns. The perturbations induce only low deviations in the movement that do not deteriorate the task and can barely be spotted by the user.

Calibrating Trust in Human-Drone Cooperative Navigation

The main contribution of this work is in the development of a long-term 12-month older adult co-design methodology for home social robot design with long-term co-design guidelines for how other researchers can adopt the methodology. Through the process, we promote co-design as a human-centered approach for designing robots, empowering older adults in the design of these technologies.

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Network Analysis of an Infant's Motor Actions Performed in a Robot-Assisted Learning Environment

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