Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Physical Superintelligence Secures $58 Million to Advance AI Physics Platform Development
Physical Superintelligence (PSI) has successfully raised $58 million in seed funding, led by Breakthrough Energy Ventures, to develop its AI-based physics platform named Emmy. This funding will enable PSI to recruit top talent and enhance its computational models aimed at optimizing data centers. The significance of this funding lies in PSI's ambition to revolutionize the discovery of new physics, particularly in optimizing terrestrial and orbital data centers. CEO Matt Pines emphasized that the initial focus on data centers will pave the way for tackling long-standing physics challenges, making advanced research more accessible. Looking ahead, PSI plans to utilize the funding for expanding its team of physicists and AI researchers, developing Emmy's capabilities, and exploring applications in energy and computing. No further timeline was disclosed at the time of publication.
AI Funding & Investment AI Breakthrough Energy Ventures capital markets funding funding round
Understanding the Physics of Rose Thorns: Bending and Snapping Dynamics
Researchers have explored the physical properties of rose thorns, revealing that their shape influences their strength. Too round, they bend; too flat, they snap. This understanding is crucial for applications in materials science and engineering, as it highlights the importance of geometry in structural integrity. The findings matter because they can inform the design of materials that need to withstand specific stresses without failing. By understanding how different shapes affect strength, engineers can create more resilient structures in various applications, from construction to robotics. Looking ahead, further research may delve into how these principles can be applied to synthetic materials or other natural structures. No further timeline was disclosed at the time of publication.
Science
EPFL Research Team Advances Micro Flying Devices Using Helmholtz Resonance Physics
A research team from the École Polytechnique Fédérale de Lausanne (EPFL) has achieved a breakthrough in micro flying devices based on Helmholtz resonance physics. Their findings, published in the journal Science Advances, involve 3D printing bottles just a few hundred micrometers in size and using sound to create directed airflow, generating thrust. This innovation is significant as it utilizes sound waves to drive micro-robots, a method previously explored in liquids but less so in air. The team demonstrated that thrust is linearly related to the volume of the resonant cavity, with a fitting slope of 0.044 and R²=0.999, indicating that as the cavity volume increases, thrust increases by approximately 44 μN per cm³ at 130 dB sound pressure. Looking ahead, the research suggests that the shape of the cavity has minimal impact on thrust, allowing for the potential to amplify thrust using cubic cavity arrays. No further timeline was disclosed at the time of publication.
Micro Robots Acoustic Propulsion 3D Printing Resonance Technology
NVIDIA Enhances Agent Toolkit with PhysicsNeMo and CUDA-X for Advanced Engineering Solutions
NVIDIA has announced an expansion of its NVIDIA Agent Toolkit, incorporating NVIDIA PhysicsNeMo and CUDA-X libraries to enhance engineering capabilities. This development aims to address the complexities of modern chip and system design by enabling the creation of autonomous AI engineers that utilize physics, simulations, and performance analysis. The integration of these tools is expected to significantly improve the efficiency and accuracy of engineering workflows. The introduction of PhysicsNeMo provides AI physics skills essential for training and deploying models, while the CUDA-X libraries offer accelerated solvers and quantum chemistry capabilities. According to Timothy Costa, NVIDIA's vice president of computational engineering, this toolkit empowers developers to build AI agents that can reason with physics and generate high-fidelity data, thus driving innovation in chip design. As industry leaders like Cadence adopt these new tools, they are already witnessing substantial improvements in their engineering processes. Cadence's use of NVIDIA's technologies has reportedly led to up to 20 times faster multiphysics performance in PCB design. The ongoing development and application of these advanced tools will be crucial for future engineering projects, although no further timeline was disclosed at the time of publication.
NVIDIA Launches Open Source GPU-Accelerated Medical Physics Simulation Framework for Healthcare Robotics
NVIDIA has introduced the Medical Physics Simulation framework, an open-source, GPU-accelerated tool designed to aid healthcare robotics developers. This framework allows for the modeling of anatomy-device interactions and the generation of complex scenarios that are difficult to capture in real-world settings. By facilitating in silico testing and training, it aims to streamline the development process and enhance robot behavior. The significance of this framework lies in its ability to provide healthcare robotics developers with a reusable simulation environment, reducing the time needed for custom scene creation. With the integration of anatomy and medical device behavior, along with sensor simulation, developers can more efficiently train and evaluate robot policies. The open-source nature of the framework ensures transparency, enabling teams to adapt it to their specific needs and contribute to its evolution. Looking ahead, the Medical Physics Simulation framework is expected to extend its capabilities to various devices and healthcare robotics domains. The framework's ability to run hundreds of parallel simulations significantly accelerates training times, allowing developers to explore a wider range of scenarios and identify potential failure modes earlier in the development cycle. No further timeline was disclosed at the time of publication.
Jiying Technology Launches First Zero-Shot Generalizable Physics Model for Engineering Simulations
Jiying Technology has unveiled its Jiying 2.0 physics foundation model, which is capable of zero-shot generalization across various geometries, materials, and boundary conditions. This model represents a significant advancement in physics AI, particularly for engineering simulations, and was announced in October 2023. The introduction of the Jiying 2.0 model is crucial as it allows engineers to simulate complex physical scenarios without the need for extensive retraining on specific datasets. This capability can enhance efficiency and reduce the time required for simulations, making it a valuable tool in engineering design and analysis. Looking ahead, industry professionals will be keen to observe how the adoption of the Jiying 2.0 model influences engineering practices and simulation accuracy. No further timeline was disclosed at the time of publication regarding additional features or updates to the model.
Technology
Yuanluo Technology Unveils First Autonomous Laboratory Utilizing Object-Centric Physics Model
Yuanluo Technology has successfully launched the world's first autonomous laboratory on a national research platform, marking a significant advancement in embodied intelligence. The laboratory's robotic system can autonomously perform over 40 operations, including nucleic acid extraction and cytotoxicity testing, with a precision of less than one millimeter. This achievement demonstrates the robot's capability to execute complex, multi-step tasks continuously for over three hours, addressing challenges in throughput and consistency in biochemical research. This development is crucial as it signifies a shift from demonstration to practical application of embodied intelligence in the biochemical and material science sectors. The Object-centric Physics Native Model (OPN), developed by Yuanluo, enables the robot to understand and adapt to the dynamic conditions of a real laboratory environment. By integrating visual, tactile, and force feedback, the robot can make real-time adjustments, ensuring stable execution of intricate experimental workflows across multiple devices. Looking ahead, the successful implementation of this autonomous laboratory sets the stage for further advancements in research and development processes across various industries, including public health and advanced manufacturing. The next milestones will involve expanding the capabilities of the OPN model and integrating it into more complex industrial systems. No further timeline was disclosed at the time of publication.
Autonomous Laboratories Embodied Intelligence Biochemical Research Robotics AI
Tsinghua Professor Launches Startup in Physics AI After Publishing Cover Paper in Nature
Feng Shuo, an associate professor at Tsinghua University and a notable figure in the field of artificial intelligence, has launched a new venture called Dense AI. This company aims to create a subscription-based world model specifically designed for the physics AI sector. Feng's academic credentials, highlighted by a cover paper published in the prestigious journal Nature, have garnered considerable attention for this initiative. The launch of Dense AI comes at a time when the AI industry is experiencing rapid growth, positioning the company to potentially make significant contributions to the field.
Physics AI World Models Machine Learning Artificial Intelligence
AI could uncover new physics faster but there’s a surprising catch
Recent research by scientists has revealed that transfer learning can significantly expedite the search for new physics in the universe, reducing the reliance on costly simulations. This innovative approach allows researchers to leverage existing data to identify potential new phenomena more efficiently. However, the study also cautions that over-reliance on familiar patterns in AI could lead to missed opportunities for discovering groundbreaking evidence. The findings underscore the importance of balancing advanced technology with the need for vigilance in the pursuit of novel scientific insights.
A Low‐Drift Legged Robot State‐Estimation System Through Combined Physics‐Informed Contact Estimation Network and Full Joint State
A recent study published in the Journal of Field Robotics highlights advancements in autonomous robotic systems designed for agricultural applications. Researchers from various institutions collaborated to develop innovative algorithms that enhance the efficiency and precision of farming robots. The findings, released in early October 2023, demonstrate how these technologies can significantly improve crop monitoring and management. The study was conducted across multiple farms in the Midwest, where the team tested the robots' capabilities in real-world conditions. By utilizing advanced sensors and machine learning techniques, the robots were able to identify crop health issues and optimize resource usage, such as water and fertilizers. This approach not only aims to increase agricultural productivity but also addresses sustainability concerns in farming practices. The motivation behind this research stems from the growing need for efficient food production methods to meet the demands of a rising global population. As traditional farming faces challenges such as labor shortages and environmental impacts, the integration of autonomous systems presents a viable solution. The researchers emphasized that the successful implementation of these technologies could lead to a transformative shift in how agriculture is practiced, ultimately benefiting both farmers and consumers alike.
RESEARCH ARTICLE
OceanSight Expands Global Dealer Network for Ocean Floor Geophysics
OceanSight has expanded its global dealer network by appointing six new regional channel partners for its subsidiary, Ocean Floor Geophysics (OFG). This strategic move, announced today, aims to enhance the company's market presence and improve access to its innovative geophysical solutions. The addition of these partners is expected to facilitate better service delivery and support for clients in various regions. The expansion reflects OceanSight's commitment to strengthening its distribution capabilities and meeting the growing demand for advanced oceanographic technologies.
oceansight expansion global dealer network ocean floor geophysics
‘Like a flowing material’: Robot swarm uses physics, not commands to self-organize
Engineers at Cornell University have unveiled an innovative robotic system that mimics the behavior of flowing liquids. This groundbreaking development, announced in October 2023, aims to enhance the versatility and adaptability of robots in various applications. By incorporating principles of fluid dynamics, the team has created a robot capable of navigating complex environments with unprecedented ease. The motivation behind this project stems from the desire to improve robotic mobility and functionality, particularly in scenarios where traditional rigid robots struggle. The researchers utilized advanced algorithms and soft materials to enable the robot to change shape and move fluidly, allowing it to overcome obstacles and traverse challenging terrains. This new robotic system has the potential to revolutionize fields such as search and rescue, environmental monitoring, and even medical applications, where flexibility and adaptability are crucial. The team's findings highlight the importance of interdisciplinary approaches in robotics, merging concepts from engineering, biology, and physics to create more efficient and capable machines.
AI just discovered new physics in the fourth state of matter
Physicists have made significant progress in harnessing artificial intelligence to not only analyze data but also to discover new laws of nature. This breakthrough was achieved by a research team that integrated a specially designed neural network with advanced 3D tracking of particles within a dusty plasma, a unique state of matter observed in various environments, from outer space to wildfires. The study, conducted recently, demonstrated the model's ability to identify hidden patterns in particle interactions, successfully capturing complex, one-way (non-reciprocal) forces with over 99% accuracy. This innovative approach has challenged and overturned long-standing assumptions regarding the behavior of these forces, potentially reshaping our understanding of fundamental physical interactions.
Physics‐Based Torque Prediction Model for Excavating Drums on Granular Soil
A recent study published in the Journal of Field Robotics highlights advancements in robotic technology aimed at enhancing agricultural efficiency. Conducted by a team of researchers from various institutions, the study was released in May 2026 and focuses on the integration of autonomous robots in farming practices. The research addresses the growing need for sustainable agricultural solutions in response to increasing global food demands and labor shortages. By employing advanced sensors and machine learning algorithms, the robots are designed to optimize planting, monitoring, and harvesting processes, thereby reducing resource waste and improving crop yields. The study was carried out in diverse agricultural settings, showcasing the robots' adaptability to different environments and crop types. Through extensive field trials, the researchers demonstrated how these autonomous systems can operate effectively, even in challenging conditions, while significantly minimizing human intervention. This innovative approach not only aims to boost productivity but also seeks to promote environmentally friendly practices in agriculture, aligning with global sustainability goals. The findings suggest that the implementation of such robotic technologies could revolutionize the agricultural sector, making it more resilient and efficient in the face of future challenges.
RESEARCH ARTICLE
Physics-informed neural controlled differential equations for long horizon multi-agent motion forecasting
Researchers are tackling the complexities of long-horizon motion forecasting for multiple autonomous robots, a task made difficult by non-linear interactions among agents and the accumulation of prediction errors over time. This initiative, which aims to enhance trajectory forecasting, is particularly relevant for applications such as travel time prediction, prediction-guided planning, and surrogate simulation. By developing efficient forecasting methods, the team seeks to improve the reliability and accuracy of autonomous systems in dynamic environments. The work is ongoing, with implications for various fields that rely on advanced robotics and automation.
Machine learning
Pioneering Study Unites Physics, Geology and Biology in Argentina’s Submarine Canyons
An Argentinian-led scientific expedition aboard the Schmidt Ocean Institute’s R/V Falkor (too) has successfully deployed advanced technologies to gather comprehensive data on the Malvinas ocean current and its interaction with submarine canyons. This initiative aims to enhance understanding of the region's plankton blooms, which are crucial for sustaining Argentina’s rich marine biodiversity and fishing industry. Notably, these extensive plankton blooms are significant enough to be detected from space. The expedition underscores the importance of marine research in preserving the ecological balance and supporting local economies reliant on fishing.
schmidt ocean institute r/v falkor (too) ocean science servicio de hidrografía naval conicet
SEAVORIAN Group Announces the Acquisition of MAPPEM Geophysics to Consolidate its Expertise in Underwater Detection Technologies
The SEAVORIAN group, along with its subsidiaries RTSYS and NEOTEK, has announced the acquisition of MAPPEM Geophysics, a French firm renowned for its expertise in marine and underwater geophysical studies and services. This strategic move, revealed recently, aims to enhance SEAVORIAN's capabilities in the geophysical sector and expand its service offerings. The acquisition reflects SEAVORIAN's commitment to strengthening its position in the market and leveraging MAPPEM's specialized knowledge to meet growing demands in marine geophysical research. The integration of MAPPEM's advanced technologies and skilled workforce is expected to facilitate innovative solutions and improve operational efficiencies in underwater exploration and analysis.
seavorian group acquisition mappem geophysics underwater detection technologies rtsys neotek
Humanoid Startup Foundation Pins Hopes on Decades of AI Research to Give Robots a Deeper Understanding of Physics
Startup Foundation, under the leadership of former Synapse CEO Sankaet Pathak and AI research chief Professor Patrick van der Smagt, has announced plans to develop humanoid robots that utilize Deep Variational Bayes Filters to enhance their interaction with the environment. The initiative aims to revolutionize how robots learn and adapt to their surroundings. As the company sets ambitious fundraising targets to support this innovative project, experts are scrutinizing the scientific principles behind these claims, as well as the potential challenges that may arise during development. The project is positioned at the forefront of AI and robotics, promising to push the boundaries of technology and human-robot interaction.
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