United States

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.

IEEE RO-MAN 2021

Calibrating Trust in Human-Drone Cooperative Navigation

monitoring of behaviour and internal states of humans; cooperation and collaboration in human-robot teams; user-centered design of robots

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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AAMAS 2021

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. 

Studies have shown that the appearance and movements of home robots may play key roles in the impression and engagement of users, opposed to recent rises in the smart speaker market. In this research, we conduct a user experiment with the aim of clarifying the elements required to evaluate the human impression of a robot's movements, based on the hypothesis that adequate movements may lead to better impressions and engagement.

Impression Evaluation of Robots Behavior When Assisting Human in a Cooking Task

We explored using human cognitive traits as predictive measures for human-robot team task performance. We developed several tests that quantify human cognitive traits, and analyzed the relationships between the test scores and performances on several human-robot team tasks.

Predicting Individual Human Performance in Human-Robot Teaming

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 developed a new rule-based dialog system, which appropriately switches responses without interfering with human speech. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. However, the system’s speech sometimes superimposes on human’s speech. To solve this problem, we installed turn-taking detection, which can detect the human speaker’s continuous speech, and a function to select response by using detection results. Practical experiment suggested that our system may enable smooth conversations.

Increasing Engagement with Chameleon Robots in Bartending Services

We introduce a Grasp Manifold Estimator (GraspME) to find object's grasp affordances directly in camera images. Predicting grasp manifolds has the advantage of generalizing to continuously infinitely many grasps, which is not the case when using other grasp representations. We approximate grasp manifolds via a set of keypoints and locate them in images using Mask R-CNN as a backbone. We rely on simulation data only and perform experiments on simple and complex objects, including unseen ones and real data.

GraspME - Grasp Manifold Estimator

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.

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Calibrating Trust in Human-Drone Cooperative Navigation

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Impression Evaluation of Robots Behavior When Assisting Human in a Cooking Task

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Multiagent Task Allocation and Planning with Multi-Objective Requirements

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Premium content

Calibrating Trust in Human-Drone Cooperative Navigation

Next from IEEE RO-MAN 2021

Impression Evaluation of Robots Behavior When Assisting Human in a Cooking Task

Similar lecture

Multiagent Task Allocation and Planning with Multi-Objective Requirements

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Impression Evaluation of Robots Behavior When Assisting Human in a Cooking Task