Human-Robot Interaction: Designing robots that can naturally interact and collaborate with humans

Authors

Ruchik Kashyapkumar Thaker

Abstract

Robot learning from demonstration (LfD) is a key research paradigm that addresses the challenge of scaling robot learning, enabling robots to acquire new knowledge without prior expertise in mechanical engineering or computer programming. This approach allows non-experts to teach robots tasks, promoting real-world applications where robots, like newborns, can learn from humans through interaction. The literature highlights the significant role of LfD in human-robot collaborative tasks, emphasizing the importance of designing communication frameworks for effective human-robot collaboration (HRC). This paper presents a comprehensive review of recent advancements in LfD, focusing on collaborative robots that benefit from improved communication channels and active learning methodologies. Additionally, the review explores how LfD enhances human-robot interaction (HRI) by increasing collaboration quality and addressing key human factors like comfort and acceptance. I examine the evolution of HRI in various domains, from industrial and hazardous environments to social interactions, and discuss the socio-economic impacts of integrating robots into human-centered tasks. Finally, I identify challenges and opportunities for future research in LfD, aiming to further improve collaboration, robot learning, and human comfort in HRC.

Keywords

Human-Robot Interaction (HRI), Learning from Demonstrations (LfD), Human-Robot Collaboration (HRC), Teleop, Active Learning in Robotics, Human Acceptance of Robots

Human-Robot Interaction: Designing robots that can naturally interact and collaborate with humans. Ruchik Kashyapkumar Thaker. 2020. IJIRCT, Volume 6, Issue 4. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2410072