# Recent Advances in Human-Robot Interactions
###### Abstract  
In recent years, the field of human-robot interaction (HRI) has developed significantly, with hundreds of scientific publications covering various research domains [2]. Specifically, one of the most rapidly advancing areas concerns the integration of artificial intelligence (AI), where significant progress is revolutionizing the way humans and robots collaborate [3]. Thanks to machine learning algorithms, robots can adapt to the behaviors of the users they interact with, offering increasingly personalized experiences [4].  
Natural language processing (NLP) enables them to understand voice commands and provide context-aware responses, making communication with humans more seamless and natural [5, 6]. Additionally, advancements in computer vision allow robots to recognize and interpret gestures and facial expressions, enhancing their perception and interaction capabilities. Finally, reinforcement learning plays a crucial role in optimizing real-time responses, allowing robots to continuously evolve based on human feedback [7]. Recently, due to these innovations, socially assistive robots (SARs) have been able to provide emotional support in healthcare settings [8].  
Another sector that has seen significant advancements is that of large language models (LLMs) [6, 9]. These developments have enhanced the conversational abilities of robots, making them more versatile in handling assigned tasks. Robots equipped with LLMs excel at building connections with individuals and show promising capabilities in interpreting complex non-verbal cues. However, they tend to exhibit shortcomings in logical communication and may induce anxiety in interlocutors [10].  
Another emerging area of innovation in HRI is intention prediction [11]. Specifically, a robot capable of anticipating the intentions of its human collaborators would be significantly more efficient than one lacking this ability as it would eliminate the need for explicit articulation of every intention [12]. Intention prediction is made possible through the analysis of human eye movements and doing so supports the precise anticipation of each step required to complete a given task. This enables the robot to promptly understand the intentions of its human counterpart [12].  
One of the most promising areas for the future of HRI is humanoid robotics. These robots, designed with a human-like structure (head, torso, arms, and legs), are intended to mimic human behaviors, adapt to dynamic environments, and make autonomous decisions [13]. The capabilities of humanoid robots stem from the integration of multiple disciplines, including mechanics, electronics, computer science, and artificial intelligence. Additionally, biology, through the study of the human brain, contributes further to the development of robots with increasingly human-like behaviors. However, the gap between human abilities and the current capabilities of humanoid robots remains substantial [14, 15].  
Although research has not yet reached its full potential, future developments in both software and hardware could lead to significant advancements. Humanoid robots could be employed in numerous sectors, including industry, healthcare, education, agriculture, and entertainment, as well as in military and rescue operations. This suggests that in the near future humans and robots will collaborate daily in workplace environments [12, 15, 16].  
The introduction of robots into the workplace has also raised new challenges. Often, human workers are required to monitor robot behaviors. This is a task that, on the one hand, can be stimulating as it involves decision-making, but on the other, may be alienating as it reduces human engagement in the production process. This situation can generate stress among workers [17, 18, 19]. A potential solution to this issue is represented by collaborative robots (co-bots), which, although less advanced than humanoid robots, are designed to work alongside humans in workplace environments [20].