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Showing posts sorted by date for query ROBOT. Sort by relevance Show all posts

Thursday, January 15, 2026

A robot learns to lip sync



Columbia Engineers build a robot that learns to lip sync to speech and song.


Columbia University School of Engineering and Applied Science

Lip Syncing Robot 

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Hod Lipson and his team have created a robot that, for the first time, is able to learn facial lip motions for tasks such as speech and singing.

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Credit: Jane Nisselson/ Columbia Engineering





New York, NY—Jan. 14, 2026—Almost half of our attention during face-to-face conversation focuses on lip motion. Yet, robots still struggle to move their lips correctly. Even the most advanced humanoids make little more than muppet mouth gestures – if they have a face at all. 

We humans attribute outsized importance to facial gestures in general, and to lip motion in particular. While we may forgive a funny walking gait or an awkward hand motion, we remain unforgiving of even the slightest facial malgesture. This high bar is known as the “Uncanny Valley.” Robots oftentimes look lifeless, even creepy, because their lips don't move. But that is about to change.

A Columbia Engineering team announced today that they have created a robot that, for the first time, is able to learn facial lip motions for tasks such as speech and singing. In a new study published in Science Robotics, the researchers demonstrate how their robot used its abilities to articulate words in a variety of languages, and even sing a song out of its AI-generated debut album “hello world_.”

The robot acquired this ability through observational learning rather than via rules. It first learned how to use its 26 facial motors by watching its own reflection in the mirror before learning to imitate human lip motion by watching hours of YouTube videos. 

“The more it interacts with humans, the better it will get,” promised Hod Lipson, James and Sally Scapa Professor of Innovation in the Department of Mechanical Engineering and director of Columbia’s Creative Machines Lab, where the work was done.

Robot watches itself talking 

Achieving realistic robot lip motion is challenging for two reasons: First, it requires specialized hardware containing a flexible facial skin actuated by numerous tiny motors that can work quickly and silently in concert. Second, the specific pattern of lip dynamics is a complex function dictated by sequences of vocal sounds and phonemes. 

Human faces are animated by dozens of muscles that lie just beneath a soft skin and sync naturally to vocal chords and lip motions. By contrast, humanoid faces are mostly rigid, operating with relatively few degrees of motion, and their lip movement is choreographed according to rigid, predefined rules. The resulting motion is stilted, unnatural, and uncanny.

In this study, the researchers overcame these hurdles by developing a richly actuated, flexible face and then allowing the robot to learn how to use its face directly by observing humans. First, they placed a robotic face equipped with 26 motors in front of a mirror so that the robot could learn how its own face moves in response to muscle activity. Like a child making faces in a mirror for the first time, the robot made thousands of random face expressions and lip gestures. Over time, it learned how to move its motors to achieve particular facial appearances, an approach called a “vision-to-action” language model (VLA).

Then, the researchers placed the robot in front of recorded videos of humans talking and singing, giving AI that drives the robot an opportunity to learn how exactly humans’ mouths moved in the context of various sounds they emitted. With these two models in hand, the robot’s AI could now translate audio directly into lip motor action.

The researchers tested this ability using a variety of sounds, languages, and contexts, as well as some songs. Without any specific knowledge of the audio clips' meaning, the robot was then able to move its lips in sync.

The researchers acknowledge that the lip motion is far from perfect. “We had particular difficulties with hard sounds like ‘B’ and with sounds involving lip puckering, such as ‘W’. But these abilities will likely improve with time and practice,” Lipson said. 

More importantly, however, is seeing lip sync as part of more holistic robot communication ability. 

“When the lip sync ability is combined with conversational AI such as ChatGPT or Gemini, the effect adds a whole new depth to the connection the robot forms with the human,” explained Yuhang Hu, who led the study for his PhD. “The more the robot watches humans conversing, the better it will get at imitating the nuanced facial gestures we can emotionally connect with.” 

“The longer the context window of the conversation, the more context-sensitive these gestures will become,” he added. 

The missing link of robotic ability

The researchers believe that facial affect is the ‘missing link’ of robotics. 

“Much of humanoid robotics today is focused on leg and hand motion, for activities like walking and grasping,” said Lipson. “But facial affection is equally important for any robotic application involving human interaction.”

Lipson and Hu predict that warm, lifelike faces will become increasingly important as humanoid robots find applications in areas such as entertainment, education, medicine, and even elder care. Some economists predict that over a billion humanoids will be manufactured in the next decade.

“There is no future where all these humanoid robots don’t have a face. And when they finally have a face, they will need to move their eyes and lips properly, or they will forever remain uncanny,” Lipson estimates.

“We humans are just wired that way, and we can’t help it. We are close to crossing the uncanny valley,” added Hu.

Risks and limits

This work is part of Lipson’s decade-long quest to find ways to make robots connect more effectively with humans, through mastering facial gestures such as smiling, gazing, and speaking. He insists that these abilities must be acquired by learning, rather than being programmed using stiff rules. 

“Something magical happens when a robot learns to smile or speak just by watching and listening to humans,” he said. “I’m a jaded roboticist, but I can’t help but smile back at a robot that spontaneously smiles at me.”

Hu explained that human faces are the ultimate interface for communication, and we are beginning to unlock their secrets.

“Robots with this ability will clearly have a much better ability to connect with humans because such a significant portion of our communication involves facial body language, and that entire channel is still untapped,” Hu said. 

The researchers are aware of the risks and controversies surrounding granting robots greater ability to connect with humans. 

“This will be a powerful technology. We have to go slowly and carefully, so we can reap the benefits while minimizing the risks,” Lipson said. 


Lip Syncing Robot Video [VIDEO] 


Tuesday, January 13, 2026

 


Bringing human dexterity to robots by combining human motion and tactile sensation



Researchers develop an adaptive motion system that allows robots to generate human-like movements with minimal data



Keio University Global Research Institute

Robotic Avatar Replicating Human Motion 

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This image depicts the real-time transfer of a human’s motion to a robotic avatar, enabling the latter to perform a dexterous task.

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Credit: Associate Professor Takahiro Nozaki from Keio University, Japan





Accelerating progress in robotic automation promises to revolutionize industries and improve our lives by replacing humans in risky, physically demanding, or repetitive tasks. While existing robots already excel in controlled environments such as assembly lines, the ultimate frontier of automation lies in dynamic environments found in tasks, such as cooking, assisting the elderly, and exploration. To realize this goal, one of the key barriers is making robots capable of adapting to touch. Unlike human hands, which intuitively adjust their grip for objects of unknown weight, friction, or stiffness, most robotic systems lack this crucial form of adaptability.

To transfer sophisticated human dexterity to machines, researchers have developed various motion reproduction systems (MRSs). These are centered around accurately recording human movements and recreating them in robots via teleoperation. However, MRSs tend to encounter problems if the properties of the object being handled change or do not match those of the recorded movement. This limits the versatility of MRSs and, in turn, the applicability of robots in general.

To address this fundamental challenge, a research team from Japan has developed a novel system designed to adaptively model and reproduce complex human motions. The study was led by Master’s student Mr. Akira Takakura from the Graduate School of Science and Technology, Keio University, and co-authored by Associate Professor Takahiro Nozaki, Department of System Design Engineering; Doctoral student Kazuki Yane; Professor Emeritus Shuichi Adachi, also from Keio University; and Assistant Professor Tomoya Kitamura from Tokyo University of Science, Japan. Their paper was published in IEEE Transactions on Industrial Electronics, a world-leading international academic journal in this field, on December 30, 2025.

The team’s core breakthrough was moving past linear modeling strategies and instead using Gaussian process regression (GPR). This is a regression technique that can accurately map complex nonlinear relationships, even with a small amount of training data. By recording human grasping motions for multiple objects, the GPR model was trained to identify the relationship between the object’s ‘environmental stiffness’ and the necessary position and force commands issued by the human. In turn, this process effectively reveals the human’s underlying motion intention, or ‘human stiffness’—allowing the robot to generate appropriate motion for objects it has never encountered. “Developing the ability to manipulate commonplace objects in robots is essential for enabling them to interact with objects in daily life and respond appropriately to the forces they encounter,” explains Dr. Nozaki.

To validate their approach, the researchers tested it against conventional MRSs, linear interpolation, and a typical imitation learning model. The proposed GPR system demonstrated significantly enhanced performance in reproducing accurate motion commands for both interpolation and extrapolation. For interpolation, which involves handling objects with a stiffness that falls within the limits of the training set, it reduced the average root-mean-square error (RMSE) by at least 40% for position and 34% for force. Meanwhile, for extrapolation of objects harder or softer than those in the training set, the results were equally robust, exhibiting a 74% reduction in position RMSE. Most importantly, the proposed method based on GPR markedly outperformed all other methods.

By accurately modeling human–object interactions with minimal training data, this new take on MRSs will help generate dexterous motion commands for a wide range of objects. This ability to capture and recreate complex human skills will ultimately enable robots to move beyond rigid contexts and toward providing more sophisticated services. “Since this technology works with a small amount of data and lowers the cost of machine learning, it has potential applications across a wide range of industries, including life-support robots, which must adapt their movements to different targets each time, and it can lower the bar for companies that have been unable to adopt machine learning due to the need for large amounts of training data,” comments Mr. Takakura.

Worth noting, this research group at Keio University has been actively engaged in research concerning the transmission, preservation, and reproduction of force-tactile feedback. Their previous efforts in this field have covered a wide range of topics, such as the reduction of data trafficmotion modeling, and haptic transplant technology. Their groundbreaking work on sensitive robotic arms and ‘avatar’ robots has been widely recognized by electronics research institutions like the IEEE, as well as by organizations such as the Government of Japan and Forbes.

 

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Reference
DOI: 10.1109/TIE.2025.3626633

 

About Keio University Global Research Institute (KGRI), Japan
The Keio University Global Research Institute (KGRI) was established in November 2016 as a research organization to bridge faculties and graduate schools across the university. KGRI aims to promote interdisciplinary and international collaborative research that goes beyond the boundaries of singular academic disciplines and international borders. It also aims to share research outcomes both in Japan and worldwide, further promoting engagement in joint research.

To achieve this goal, KGRI has set up more than 40 centers and projects funded by external sources or through internal grants, covering a wide range of research topics from basic research to addressing social challenges facing the world. In 2022, Keio University set its goal of becoming a "Research university that forges the common sense of the future".

Website: https://www.kgri.keio.ac.jp/en/index.html

 

About Associate Professor Takahiro Nozaki from Keio University
Dr. Takahiro Nozaki received his B.E., M.E., and Ph.D. from Keio University, Yokohama, Japan, in 2010, 2012, and 2014, respectively. In 2014, he joined Yokohama National University, Yokohama, Japan, as a Research Associate. In 2015, he joined Keio University, where he is currently an Associate Professor. He was also a Visiting Scientist with the Massachusetts Institute of Technology, Cambridge, USA, from 2019 to 2021. He was one of the winners of the IEEE Industrial Electronics Society Under-35 Innovators Contest in 2019.

https://nozaki-lab.jp/

https://k-ris.keio.ac.jp/html/100011714_en.html

 

About Mr. Akira Takakura from Keio University
Mr. Akira Takakura received a B.E. degree in System Design Engineering from Keio University, Yokohama, Japan, in 2024. He is currently working toward an M.E. degree. His research interests include adaptive control, system identification, robotics, and haptics.

 

Funding information
This work was supported by JSPS (Grant No. 16H06079) and NEDO (Project No. P15009, “Development of Core Technologies for Next-Generation AI and Robotics”).