--Facial expressiveness plays a crucial role in a robot's ability to engage and interact with children. Prior research has shown that expressive robots can enhance child engagement during human-robot interactions. However, many robots used in therapy settings feature non-personalized, static faces designed with traditional facial feature considerations, which can limit the depth of interactions and emotional connections. Digital faces offer opportunities for personalization, yet the current landscape of robot face design lacks a dynamic, user-centered approach. Specifically, there is a significant research gap in designing robot faces based on child preferences. Instead, most robots in child-focused therapy spaces are developed from an adult-centric perspective. We present a novel study investigating the influence of child-drawn digital faces in child-robot interactions. This approach focuses on a design activity with children instructed to draw their own custom robot faces. We compare the perceptions of social intelligence (PSI) of two implementations: a generic digital face and a robot face, personalized using the user's drawn robot faces. The results of this study show the perceived social intelligence of a child-drawn robot was significantly higher compared to a generic face.

A smiling face made from living human skin could one day be attached to a humanoid robot, allowing machines to emote and communicate in a more life-like way, say researchers. Its wrinkles could also prove useful for the cosmetics industry. The living tissue is a cultured mix of human skin cells grown in a collagen scaffold and placed on top of a 3D-printed resin base. Unlike previous similar experiments, the skin also contains the equivalent of the ligaments that, in humans and other animals, are buried in the layer of tissue beneath the skin, holding it in place and giving it incredible strength and flexibility. This robot predicts when you're going to smile – and smiles back Michio Kawai at Harvard University and his colleagues call these ligament equivalents "perforation-type anchors" because they were created by perforating the robot's resin base and allowing tiny v-shaped cavities to fill with living tissue.

Animatronic robots aim to enable natural human-robot interaction through lifelike facial expressions. However, generating realistic, speech-synchronized robot expressions is challenging due to the complexities of facial biomechanics and responsive motion synthesis. This paper presents a principled, skinning-centric approach to drive animatronic robot facial expressions from speech. The proposed approach employs linear blend skinning (LBS) as the core representation to guide tightly integrated innovations in embodiment design and motion synthesis. LBS informs the actuation topology, enables human expression retargeting, and allows speech-driven facial motion generation. The proposed approach is capable of generating highly realistic, real-time facial expressions from speech on an animatronic face, significantly advancing robots' ability to replicate nuanced human expressions for natural interaction.

Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by humans. In order to adapt robot behavior in real time to different situations that arise when interacting with human subjects, robots need to be able to train themselves without requiring human labels, as well as make fast action decisions and generalize the acquired knowledge to diverse and new contexts. We addressed this challenge by designing a physical animatronic robotic face with soft skin and by developing a vision-based self-supervised learning framework for facial mimicry. Our algorithm does not require any knowledge of the robot's kinematic model, camera calibration or predefined expression set. By decomposing the learning process into a generative model and an inverse model, our framework can be trained using a single motor babbling dataset. Comprehensive evaluations show that our method enables accurate and diverse face mimicry across diverse human subjects. The project website is at http://www.cs.columbia.edu/~bchen/aiface/

Robots, especially humanoid robots, never fail to fascinate people. For years, robotic companies have been trying to perfect this technology, from algorithms down to aesthetics. From building robots that look like nothing but sheer metals, most robotics companies are now into developing robots that appear like real humans, head to toe. You may know Sophia, the humanoid robot with humor like a human. Aside from its unbelievable intelligence, the female robot has also a human-like face that can show off different emotions.

British healthcare workers are hostile to their robotic co-workers, committing "minor acts of sabotage" such as standing in their way, according to a recent study by De Montfort University, which chided the humans for "not playing along with" their automated peers. The researchers contrasted the "problematic" British attitude with that of Norwegian workers, who embraced their silicon colleagues, even giving them friendly nicknames. Some 30 percent of UK jobs will be lost to automation within 15 years if current trends continue apace, according to PricewaterhouseCoopers. The percentage is even greater in the US (38 percent) as well as Germany and France (37 percent), but falls to 25 percent in Scandinavian countries like Norway and Finland. Perhaps this explains the difference in workplace interactions between the British and the Norwegians - the latter aren't as worried about losing their jobs to an electronic interloper.

In recent years, an increasing number of robots have relied on screens rather than physical mechanisms to generate expressive faces. Screens are cheap, they're easy to work with, and they allow for nearly unlimited creativity. Consequently, there's an enormous variety of robot faces, with a spectrum of similarities and differences both obvious and subtle. However, there hasn't been a comprehensive study of the entire design space, possibly because of how large it is, and this is bad, because there's a lot to learn. At the ACM/IEEE International Conference on Human Robot Interaction (HRI) last month, roboticists from the University of Washington in Seattle presented a paper entitled "Characterizing the Design Space of Rendered Robot Faces."