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A Visualization Approach for Analyzing Decision-Making in Human-Robot Interactions
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Author(s): Madhavi Godbole (Apolisrises Inc., USA), Tirupathi Rao Bammidi (Mphasis Corp., USA)and Anil Kumar Vadlamudi (Aryadit Solutions, USA)
Copyright: 2024
Pages: 18
Source title:
Explainable AI Applications for Human Behavior Analysis
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Institute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-1355-8.ch018
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Abstract
In an era where human-robot interactions are becoming increasingly integrated into our daily lives, gaining insights into the decision-making processes of robots is paramount. This chapter introduces an innovative visualization approach designed to cater specifically to the analysis and comprehension of decision-making mechanisms in human-robot interactions. This methodology combines cutting-edge visualization techniques with valuable insights from the field of robotics, creating an intuitive platform for users. This platform allows for a transparent and accessible understanding of the underlying mechanisms that govern robot behaviour. The significance of transparency in robot decision-making cannot be overstated. It fosters trust between humans and robots, which is essential for effective and seamless collaboration across various environments. By offering this level of transparency, this approach paves the way for more harmonious interactions between humans and their robotic counterparts, whether it's in industrial settings, healthcare, or everyday life. The visualization techniques employed in this approach enable users to dissect and interpret the intricate decision-making processes of robots. This includes understanding how sensors, algorithms, and environmental data contribute to the actions taken by robots. By gaining insights into these processes, users can better predict and anticipate robot behaviour, which is crucial for ensuring safety and efficiency in human-robot collaborative tasks. Also, this approach bridges the gap between the complex inner workings of robots and the human operators who interact with them. It promotes trust, enhances collaboration, and empowers users to harness the full potential of human-robot partnerships. As we continue to integrate robots into our daily lives, understanding and visualizing their decision-making processes will be instrumental in achieving seamless and productive interactions.
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