Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Attribute-based object grounding and robot grasp detection with spatial reasoning
Researchers are addressing a critical challenge in human-robot interaction by developing methods that allow robots to grasp objects identified through natural language. This advancement is crucial as it enhances the effectiveness of communication between humans and robots. Current techniques often fall short when dealing with open-form language expressions and typically require clear identification of target objects, which can lead to confusion when duplicates are present. Additionally, many existing solutions depend on expensive, detailed pixel-wise annotations for both object recognition and grasping capabilities. The ongoing work aims to simplify these processes, making it easier for robots to understand and act on verbal instructions in real-world scenarios.
Computer vision
MIT and York University Study Visual Learning in the Brain Using Neural Networks
Researchers at MIT’s McGovern Institute for Brain Research and York University in Toronto have investigated how visual learning occurs in the brain. By analyzing neural activity and utilizing computational modeling, they compared the learning processes of animals and an artificial neural network designed to mimic brain architecture. Their findings, published on July 8 in Nature Communications, reveal that changes in visual processing are crucial for learning to discriminate new objects. This research is significant as it enhances our understanding of the brain's adaptability and the mechanisms behind visual learning. The study suggests that while the overall activity patterns in the inferior temporal cortex remain stable, subtle changes occur in response to learned object recognition. These insights could inform educational strategies and improve learning outcomes across various contexts. Looking ahead, the researchers aim to further explore how these modest changes in neural activity contribute to learning. They believe that artificial neural networks can provide valuable insights into biological learning processes, potentially leading to new experimental approaches and predictions that extend beyond current understanding. No further timeline was disclosed at the time of publication.
Research Neuroscience Learning Brain and cognitive sciences Computer modeling Vision
Seoul University Introduces Single-Layer Artificial Skin for Enhanced Robotic Sensory Perception
On July 10, a research team led by Professor Seung Hwan Ko at Seoul University published a significant study in Nature Materials, unveiling a novel single-layer artificial skin. This innovative material allows robots to simultaneously sense temperature and pressure, mimicking human sensory capabilities. The design utilizes a silver-core-copper oxide shell nanowire network, enabling rapid switching between temperature and mechanical sensing modes at a frequency of 16 Hz. The development is crucial as it addresses the limitations of existing artificial skin technologies, which typically rely on multiple stacked sensors, resulting in complex structures and slower response times. The new sensor demonstrates remarkable response speeds, with mechanical stimuli detected in microseconds and thermal stimuli in milliseconds. When combined with AI models, the sensor's accuracy in object recognition improved from 65% to 95% by integrating signals from both sensing modes, showcasing its potential for real-world applications. Looking ahead, the research team has created a multi-array platform that can measure temperature and pressure distribution with spatial resolution comparable to human skin. This technology not only serves as a fingertip sensor but also has the potential to evolve into a comprehensive artificial skin system for robots. The team emphasizes that this advancement is a key enabling technology for physical AI systems, allowing machines to perceive and interact with their environment more effectively. No further timeline was disclosed at the time of publication.
Artificial Skin Robotics Sensor Technology AI Human-Robot Interaction
New color-changing tactile sensor gives robots a real-time sense of touch
Researchers have developed an innovative color-changing tactile sensor that enables machines to perceive and respond to their surroundings in real-time. This groundbreaking technology was unveiled in October 2023 and represents a significant advancement in the field of robotics and artificial intelligence. The sensor mimics the way humans and animals sense touch and texture, providing machines with the ability to "see" and interpret the materials they come into contact with. The motivation behind this development lies in enhancing the interaction between machines and their environment, allowing for more sophisticated and responsive robotic systems. By integrating this tactile sensor, robots can better understand the properties of objects, leading to improved performance in various applications, such as manufacturing, healthcare, and service industries. The process involves a combination of advanced materials and engineering techniques that allow the sensor to change color based on the pressure and texture of the surfaces it touches. This visual feedback not only aids in object recognition but also enhances the machine's ability to make informed decisions based on tactile information. As this technology continues to evolve, it holds the potential to revolutionize how machines interact with the world, paving the way for smarter, more adaptable robotic systems that can operate effectively in diverse environments.
AI and Robotics
Robots move beyond mapped worlds with advanced lidar sensing
A lidar technology company has partnered with a robotics AI developer to enhance the capabilities of autonomous systems. This collaboration, announced recently, aims to integrate advanced lidar sensing with cutting-edge artificial intelligence to improve navigation and object recognition in various applications, including autonomous vehicles and drones. The partnership is expected to leverage the strengths of both companies, combining precise environmental mapping with intelligent decision-making algorithms. This initiative comes in response to the growing demand for more reliable and efficient autonomous solutions in industries such as transportation and logistics. The companies plan to begin testing their integrated systems in the coming months, with the goal of launching a prototype by early next year.
Generative AI improves a wireless vision system that sees through obstructions
Researchers have developed an innovative technique that enables robots to more accurately detect hidden objects and interpret indoor environments by utilizing reflected Wi-Fi signals. This advancement, which leverages existing wireless technology, could significantly enhance the capabilities of robots in various applications, including search and rescue operations, home automation, and security surveillance. The technique was unveiled in October 2023, showcasing the potential for robots to navigate and understand complex indoor spaces without the need for additional sensors. By analyzing how Wi-Fi signals bounce off objects, the robots can create a detailed map of their surroundings, improving their situational awareness and object recognition skills. This breakthrough not only promises to make robots more efficient but also opens new avenues for integrating smart technology into everyday life.
RoboScience Unveils First Cloud-Based Large Model for Robotics at WAIC
At the WAIC, RoboScience showcased a groundbreaking cloud-based embodied large model named Visics, capable of controlling multiple robotic hands. This innovation allows for seamless switching between different robotic hands while maintaining operational efficiency, demonstrating the ability to recognize and grasp various objects autonomously within 30 seconds. The significance of this development lies in its potential to revolutionize robotic operations across diverse applications. By enabling a single model to adapt to various hand configurations, Visics enhances the versatility of robotic systems, allowing them to perform complex tasks without the need for extensive retraining when hardware changes occur. Looking ahead, the industry will be keen to observe how Visics performs in real-world scenarios and its ability to execute long-term tasks by integrating multiple actions. No further timeline was disclosed at the time of publication.
Robotics Cloud Computing AI Automation Object Recognition
Ant Group Launches Open Source LingBot-Vision: A Breakthrough in Robotic Vision Technology
Ant Group's Robbyant has introduced the LingBot-Vision model, a groundbreaking visual perception technology designed for robots. This innovative model, which operates with just 1.1 billion parameters, significantly outperforms traditional systems in depth estimation and object boundary recognition. By enhancing robots' ability to comprehend intricate environments, LingBot-Vision represents a major leap forward in the field of embodied intelligence. The technology has been made available as open-source, fostering further advancements and collaboration within the robotics community.
Robotic Vision Depth Perception AI Technology Open Source Embodied Intelligence
IEEE Honors Robotics Pioneer Toshio Fukuda
Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics, writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation.Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later.Toshio FukudaEmployerEgypt-Japan University of Science and Technology, in Alexandria TitleProfessor and vice president of research Member gradeLife Fellow Alma matersWaseda University, in Tokyo; University of Tokyo An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University, in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology, in Alexandria, Egypt.Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role.He’s a former program director of Japan’s Moonshot program, which by 2050 intends to develop advanced AI robots.Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022.IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board. Fukuda received the award on 24 April at a ceremony in New York City.As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them.“It’s very interesting to be on the receiving end,” he says.The journey into robotics researchAs a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines.“It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering.He earned a bachelor’s degree in engineering in 1971 from Waseda University, in Tokyo. He says one of his professors there—Ichiro Kato, regarded as the father of Japanese robotics research—was a good mentor who made a positive impact.Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems.He went on to earn a master’s degree and a doctorate in science from the University of Tokyo, in 1971 and 1977. During those years, he also attended Yale, where he conducted research on advanced control theory in 1973.He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.”“IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.”While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him.But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat.That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says.After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology, in Tsukuba.“There was a lot of research going on at the lab, including practical robotics and theory,” he says.He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart, in Germany. During his year there, he studied systems, software problems, and related topics.He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science. He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says.His work drew interest from chemical, oil, and utility companies.“I got a lot of money from them for this very practical application, which funded my research,” he says, laughing.Developing popular robotic systemsFukuda grew tired of making those robots, he says, so he switched to creating ones for scientific applications. He developed many techniques, but he probably is best known for his modular, cellular robotic systems (CEBOTs), which he introduced in 1985.He has described how CEBOTs work in numerous papers published in the IEEE Xplore Digital Library.The CEBOT system is composed of a number of autonomous robotic cells that stick together like interlocking Lego plastic bricks, he says.Each cell is a fundamental modular unit that has a function. When a simple task is given, the system can analyze it and generate the structure of the cellular manipulator. The cells connect to and detach from each other through connection mechanisms and cooperate mutually, creating complex structures and configurations.“You start developing from the component-wise to the cell-wise to a small functional unit—and then you come up with clusters that make bigger systems. We can make a society of robot beings like that,” he explained in his oral history published on the Engineering and Technology History Wiki. “It’s a distributed robotic system, a self-organized robotic system, and also an evolutionary robotic system.“It’s also a fault-tolerant robot system because if something is wrong, you just remove those things and make a new one. You keep the system working. That’s a great thing.”Today CEBOTs are used for a variety of tasks such as delivering medication in hospitals, assisting with planting crops, and transporting products in distribution centers. Check out IEEE Spectrum’s Robots Guide for news from the world of robotics.In 1989 Fukuda joined Nagoya University as a professor of mechanical engineering and micro-nano systems engineering. During his 24-year career there, he was director of the university’s Center for Micro-Nano Mechatronics. He developed a long list of technologies at the university, including many for medical applications. He also conducted groundbreaking research into intelligent robotic systems and micro- and nano-robotics.Another technology he is known for is brachiation robots, which he helped develop in 1988. He calls them monkey robots because they’re based on the pendulum-like movement of monkeys swinging from tree to tree. The gravity-based locomotion enables continuous movement.Brachiation robots now are inspecting high-voltage transmission towers and bridges, searching damaged buildings for survivors, and performing maintenance on pipelines and cables.Fukuda retired from the university in 2013 and was named professor emeritus.He didn’t stay retired for long, though. He next held a teaching appointment at Meijo University, in Nagoya, until he left in 2022 to join the Egypt-Japan University.A prominent volunteerHe joined IEEE in 1980 at the encouragement of one of his research advisors, Professor Fumio Harashima, now an IEEE Life Fellow. After attending conferences and reading the organization’s publications, Fukuda says, he looked forward to becoming more involved.“I wanted to know how to organize a conference and how to edit a paper for one of its Transactions,” he says. “I wanted to know what was going on from inside the organization, not just the outside.”In 1988 he was the founding chair and organizer of IROS, in Tokyo. The conference had 330 attendees that year, and was supported by Harashima. Today it is one of the largest and most prestigious conferences on the topic, attracting more than 9,000 people annually. Out of 120,000 conferences, it was the only conference in the Nature Index database for this year, Fukuda says.In 1996 he and other members launched IEEE Transactions on Mechatronics.He was the founding president of the IEEE Nanotechnology Council, which was established in 2002. He is considered a pioneer in nanotechnology research, particularly regarding how it relates to robotics.Over the years, he has held numerous volunteer positions on IEEE editorial boards and committees.He was the 1998–1999 president of the IEEE Robotics and Automation Society, becoming the first non-U.S. member to hold the title.He was director of IEEE Division X (2001–2002 and 2017–2018), which covers intelligent systems, biological engineering, robotics, control systems, and photonic technologies. He served as the 2013–2014 director of IEEE Region 10 (Asia-Pacific).As the 2020 IEEE president, Fukuda saw the organization through the early part of the COVID-19 pandemic. Because of travel restrictions, he realized IEEE should change how it offered its in-person services, specifically educational programs. He encouraged IEEE Educational Activities to develop an online learning platform. The IEEE Learning Network started with just three courses and now offers nearly 2,000 courses, webinars, and learning materials.An award-winning memberThe Emberson Award joins a slew of other recognitions Fukuda has received from IEEE. They include several from the IEEE Robotics and Automation Society: a 2004 Pioneer Award, a 2009 Saridis Leadership Award, and the 2011 Harashima Award for Innovative Technologies. He is also a recipient of the Board-level 2010 IEEE Robotics and Automation Technical Field Award.He says he feels strongly that IEEE should be a diverse organization that is welcoming to all. As IEEE president, he led efforts to devise a diversity, equity, and inclusion program. Several policies, procedures, and bylaws were revised to give members a safe, inclusive place for discourse.“It’s important for IEEE to make everyone feel comfortable,” he says. “DEI programs are important. All people should be equal. IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.“It accepted me, from the Far East. That’s why I like it.”You can learn more about Fukuda and his career from the oral history conducted by the IEEE History Center.
Robotics Robots Ieee-member-news Type-ti Ieee-awards Toshio-fukuda
RoboScience Advances Embodied Intelligence Towards the VLOA Era at ICRA
RoboScience is making strides in the field of embodied intelligence with its innovative Vision-Language-Object-Action (VLOA) framework, designed to improve robotic capabilities by enabling robots to predict changes in objects within dynamic environments. The company recently gained notable recognition at the International Conference on Robotics and Automation (ICRA), where it presented groundbreaking research focused on robotic manipulation and dual-arm collaboration. This research aims to tackle the complexities faced in real-world applications, positioning RoboScience at the forefront of advancements in robotics.
Embodied Intelligence Robotic Manipulation Dual-Arm Robotics AI VLOA
Boston Dynamics and Google DeepMind Teach Spot to Reason
Boston Dynamics has announced that its quadruped robot, Spot, is now equipped with Google DeepMind’s Gemini Robotics-ER 1.6, a high-level embodied reasoning model designed to enhance the robot's usability and intelligence for complex tasks. This development, revealed today, marks a significant advancement in the commercial deployment of legged robots, particularly in industrial inspections, where Spot will autonomously identify hazardous debris, read gauges, and utilize vision-language-action models for better environmental understanding. The collaboration aims to improve how robots interpret and interact with their surroundings, addressing the challenges of ensuring that robotic actions align with human reasoning. Marco da Silva, vice president of Spot at Boston Dynamics, emphasized that the new capabilities will allow Spot to autonomously navigate real-world challenges more effectively. Despite the progress, experts acknowledge ongoing challenges in achieving seamless human-robot interaction. Carolina Parada from Google DeepMind noted that while the Gemini model enhances visual recognition, it currently lacks integration with other sensory data, such as touch, which is crucial for reliable object manipulation. As part of the deployment, customers using Spot for inspections will need to share operational data with Boston Dynamics to further refine the technology. The introduction of Gemini Robotics-ER 1.6 is seen as a step toward creating safer and more reliable robots capable of performing everyday tasks, with the potential to apply these advancements to other robotic platforms in the future.
Boston-dynamics Spot-robot Google-deepmind Inspection-robots Quadruped-robots
The science of human touch – and why it’s so hard to replicate in robots
Researchers at the University of Oxford are exploring the advancements in robotic technology, highlighting the significant progress robots have made in visual recognition and navigation. These machines can now identify objects and maneuver through complex environments, sorting thousands of parcels each hour. However, challenges remain when it comes to delicate interactions; robots struggle to perform tasks that require gentle, safe, or meaningful touch. This research aims to bridge the gap between current capabilities and the nuanced skills necessary for robots to interact more effectively with their surroundings and human counterparts. As the field evolves, understanding and overcoming these limitations will be crucial for the future integration of robots in everyday tasks.
Doosan Robotics to Unveil 'AI Robot Solution' at Automatica 2025
Doosan Robotics has announced its participation in Automatica 2025, Europe’s leading automation exhibition, set to take place in Munich, Germany, starting June 24. The company will showcase its innovative 'AI Robot Solution' across two exhibition zones: 'Automation in Action' and 'Automation to Reality.' In the 'Automation to Reality' area, attendees will have the opportunity to explore a variety of AI-integrated robotic solutions, including technologies developed in collaboration with NVIDIA and AWS. Among the highlights is 'Voice to Real,' a voice-recognition solution co-developed with AWS, and an enhanced version of *Mixmaster Moodie*, which features a 3D vision camera to understand everyday language and execute tasks autonomously. The Material Handling Solution will demonstrate three collaborative robots capable of recognizing and manipulating objects without prior training, utilizing Doosan's Multi-Arm Dynamic Manipulation Engine for complex tasks. Additionally, the Sanding Solution will enable robots to autonomously polish intricate surfaces, while the Inspection Solution will conduct real-time vehicle inspections using a 3D scanning system. Another key feature is "Sim to Real," a motion control solution that leverages NVIDIA Isaac Sim for simulating AI-driven robots, allowing for rapid computation of robot trajectories and seamless transfer to physical robots. The 'Automation in Action' zone will exhibit practical applications of robotics in manufacturing processes such as welding, assembly, and quality inspection. These solutions, co-developed with European partners, are already in use by major companies like General Motors, Danone, Heineken, and Royal Mail, highlighting their commercial viability in the European market.
Miller and co-authors receive award at CVPR 2024
Bailey Miller, a PhD student in computer science, along with co-authors Hanyu Chen, Alice Lai, and Ioannis Gkioulekas, has been recognized with an honorable mention for best student paper at the 2024 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), which took place in Seattle, Washington. Their award-winning paper, titled “Objects as volumes: A stochastic geometry view of opaque solids,” presents a novel theoretical framework aimed at enhancing the understanding of opaque solids through the lens of stochastic geometry. This recognition highlights the innovative contributions of the authors to the field of computer vision and pattern recognition.
Uncategorized
Visual Language Models Train Robots to Read Human Emotions
A recent study led by Seung Chan Hong at the University of Melbourne explores the emotional capabilities of collaborative robots as they increasingly work alongside humans. Published on May 18 in IEEE Robotics and Automation Letters, the research investigates how robots can better understand human emotions through contextual cues, beyond just facial expressions. Involving 40 volunteers, the study trained a vision language model (VLM) to interpret emotions based on video interactions where robots handed objects to humans. The VLM outperformed traditional AI systems, scoring 0.86 in emotional accuracy compared to 0.77 for conventional methods. This improvement is attributed to the VLM's ability to consider the entire context of interactions rather than isolated facial expressions. In a follow-up experiment, participants interacted with a robot that was programmed to make an error, receiving either an emotionally adaptive apology or a standard one. The majority preferred the adaptive response, but trust in the robot diminished after it failed to complete its task, highlighting that emotional responses cannot compensate for a lack of functionality. While the VLM effectively recognized emotions from a third-party perspective, its accuracy dropped when compared to participants' self-reported feelings, indicating that robots still struggle to fully understand human emotions. The findings suggest that while emotional adaptivity is valuable, the primary concern for users remains the robot's competence in performing tasks.
Robotics Journal-watch Ai-models Emotion-recognitionRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
