Titlebook: Discovering the Frontiers of Human-Robot Interaction; Insights and Innovat Ramana Vinjamuri Book 2024 The Editor(s) (if applicable) and The

degradation

capture both temporal and spatial dependencies in EEG data. The chapter then delves into practical applications of these models in real-world BCI systems, discussing how they translate into tangible benefits for users. We explore prospects and ongoing research aimed at overcoming limitations like c

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e AI for imparting intelligence to robot grasping. This chapter presents our recent research and its application in this exciting domain of vision based robotics. Although we have presented our works on tabletop environments, the similar strategies can be scaled up for 6-D pose as well. Given the da

AGONY

https://doi.org/10.1057/9781403978585odels are considered for supervised object affordance classification without having affordance heatmaps as teaching signal. The output of these models obtained after the experimentation over modified CAD-120 dataset is fed to smooth grad-cam. for post hoc explainability analysis. These experiments l

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yond mere control. It facilitates the monitoring of human mental states during tasks, which is of significant interest in Human–Robot Collaboration (Roy et al., Robotics 9(4):100, 2020). A robot equipped to monitor human mental states could dynamically adjust its behavior to uphold an optimal qualit

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Gregory J. Hamlin,Arthur C. SandersonNetwork) for prediction, while explaining the model prediction outcome and analyzing the feature importance for each feature through different XAI methods. Specifically, the LIME (Local Interpretable Model Agnostic Explanation), SHAP (Shapley Additive exPlanations), ELI5 (Explain Like I’m 5), and Pa

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Value Alignment and Trust in Human-Robot Interaction: Insights from Simulation and User Study,ject study to answer these questions. Results from the simulation study show that alignment of values is important for trust when the overall risk level of the task is high. We also present an adaptive strategy for the robot that uses Inverse Reinforcement Learning (IRL) to match the values of the r