Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot’s behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of Human-Robot Interaction, Social Robotics and Psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Secondly, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements are presented as an outcome of the review.

A human-shaped robotic hand offers unparalleled versatility and fine motor skills, enabling it to perform a broad spectrum of tasks with precision, power, and robustness. Across the paleontological record and animal kingdom, we see a wide range of alternative hand and actuation designs. Understanding the morphological design space and the resulting emergent behaviors can not only aid our understanding of dexterous manipulation and its evolution but also assist with design optimization, achieving and ultimately surpassing human capabilities. Exploration of hand embodiment has, to date, been limited by challenges of accessibility in customizable hands in the real world and by the reality gap in simulation of complex interactions. We introduce an open parametric design that integrates techniques for simplified customization, fabrication, and control with design features to maximize behavioral diversity. Nonlinear rolling joints, anatomical tendon routing, and a low–degree-of-freedom modulating actuation system enable rapid production of single-piece 3D-printable hands without compromising dexterous behaviors. To demonstrate this, we evaluated the low-level behavior range and stability of the design, showing variable stiffness over two orders of magnitude. In addition, we fabricated three hand designs: human, mirrored human with two thumbs, and aye-aye hands. Manipulation tests evaluated the variation in each hand’s proficiency at handling diverse objects and demonstrated emergent behaviors unique to each design. Overall, we introduce diverse designs for robotic hands, provide a design space to compare and contrast different hand morphologies and structural configurations, and share a practical and open-source design for investigating embodied manipulation.

To generate context-aware behaviors in robots, robots are required to have a careful evaluation of its encounters with humans. Unwrapping emotional hints in observable cues in an encounter will improve a robot’s etiquettes in a social encounter. This article presents an extended human study conducted to examine how several factors in an encounter influence a person’s preferences upon an interaction at a particular moment. We analyzed the nature of conversation preferred by a user considering the type of conversation a robot could have with its user, having the interaction initiated by the robot itself. We took an effort to explore how such preferences differ as the factors present in the surrounding alter. A social robot equipped with the capability to initiate a conversation is deployed to conduct the study by means of a wizard-of-oz (WoZ) experiment. During this study, conversational preferences of users could vary from “no interaction at all” to a “long conversation.” We changed three factors in an encounter which can be different from each other in each circumstance: the audience or outsiders in the environment, user’s task, and the domestic area in which the interaction takes place. Conversational preferences of users within the abovementioned conditions were analyzed in a later stage, and critical observations are highlighted. Finally, implications that could be helpful in shaping future social human-robot encounters were derived from the analysis of the results.

Intelligent robot companions contribute significantly to improve the living standards of people in the modern society. Therefore, humanlike decision-making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its human user. On the other hand, the robot will have the capability of dealing with humans without disturbing them by its behavior. Perception of Behavioral Ontology prior to an interaction is an important aspect in this regard. Furthermore, humans make an instant evaluation of task-related movements of others before approaching them. In this article, we present a mechanism to monitor how the activity space is utilized by a particular user on a temporal basis as an ontological assessment of the situation and then determine an appropriate approach behavior for a proactive robot to initiate an interaction with its user. This evaluation was then used to determine appropriate proxemic behavior to approach that person. The usage of activity space varies depending on the task of an individual. We used a probabilistic approach to find the areas that are the most and least likely to be occupied within the activity space of a particular individual during various tasks. As the robot approaches its subject after analyzing the spatial behavior of the subject within his/her activity space, spatial constraints occurred as a result of which robot’s movement could be demolished. Hence, a more socially acceptable spatial behavior could be observed from the robot. In other words, an etiquette based on approach behavior is derived considering the user’s activity space. Experiment results used to validate the system are presented, and critical observations during the study and implications are discussed.

Diagnostic errors remain a major cause of preventable deaths, particularly in resource-limited regions. Medical training simulators, including robopatients, play a vital role in reducing these errors by mimicking real patients for procedural training such as palpation. However, generating multimodal feedback, especially auditory pain expressions, remains challenging due to the complex relationship between palpation behavior and pain sound. The high-dimensional nature of pain sounds makes exploration challenging with conventional methods. This study introduces a novel experimental paradigm for pain expressivity in robopatients where they dynamically generate auditory pain expressions in response to palpation force, by co-optimizing realtime human feedback using machine learning. Using Proximal Policy Optimization (PPO), a reinforcement learning (RL) technique optimized for continuous adaptation, our robot iteratively refines pain sounds based on real-time human feedback. This robot initializes randomized pain responses to palpation forces, and the RL agent learns to adjust these sounds to align with human preferences. The results demonstrated that the system adapts to an individual’s palpation forces and sound preferences and captures a broad spectrum of pain intensities, from mild discomfort to acute distress. The study further showed that pain sound perception exhibits saturation at lower forces with gender-specific thresholds. Thus, this study presents an early attempt to use human-in-the-loop reinforcement learning to co-optimize haptic input and auditory pain expression. These findings highlight the system’s potential to enhance abdominal palpation training by offering a controllable and immersive simulation platforms.