United States

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.

IEEE RO-MAN 2021

Network Analysis of an Infant&#39;s Motor Actions Performed in a Robot-Assisted Learning Environment

motor action

infant

social network

rehabilitation

child-robot interaction

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INTERMAG 2021

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

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. 

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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?

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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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