Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Self-refining vision language model for robotic failure detection and reasoning
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing
Automated reasoning
Large Tabular Models Excel Where LLMs Fail
A new generative AI model, known as NEXUS, has emerged from the startup Fundamental, which recently secured $275 million in funding. Launched on February 5, 2026, NEXUS is designed to analyze structured data, a task that traditional large language models (LLMs) like ChatGPT and Claude struggle with. While LLMs excel in generating human-like text and images, they falter when faced with complex tabular data, which is crucial for businesses across various sectors, including finance and healthcare. Fundamental's CEO, Jeremy Fraenkel, explained that LLMs are not suited for structured data due to their reliance on sequential input, making them less effective for tasks requiring deterministic predictions, such as fraud detection. In contrast, NEXUS utilizes a large tabular model (LTM) that directly models the structure of tabular data, allowing for more accurate reasoning and predictions. The development of NEXUS involved training on billions of tables, using a mix of proprietary and public datasets while ensuring customer data confidentiality. This innovative model has already been integrated into Amazon Web Services' SageMaker platform, enhancing its accessibility for businesses handling sensitive data. As the demand for effective data analysis solutions grows, other companies, including Feedzai and Google, are also developing similar technologies. Experts predict that the future of data processing will increasingly rely on automated systems, combining the strengths of LLMs and LTMs to improve efficiency and accuracy in data analysis.
Data-analytics Llms Foundation-models Databases
Gill Pratt Says Humanoid Robots’ Moment Is Finally Here
In 2012, the U.S. Defense Advanced Research Projects Agency (DARPA) launched the DARPA Robotics Challenge (DRC), a multimillion-dollar competition aimed at advancing disaster robotics. Gill Pratt, the architect of the DRC and now CEO of the Toyota Research Institute (TRI), envisioned the challenge as a catalyst for significant progress in robotics, similar to earlier DARPA initiatives that revolutionized driverless cars. A decade later, Pratt believes humanoid robots are on the brink of a transformative breakthrough, largely due to advancements in artificial intelligence (AI). Pratt notes that while the physical capabilities of humanoid robots have improved, the real change lies in their cognitive abilities. Recent AI developments allow robots to learn tasks through demonstration rather than programming, although data availability remains a challenge. He emphasizes the need for robots to develop deeper reasoning capabilities, beyond mere pattern recognition, to navigate complex real-world scenarios effectively. At TRI, Pratt's team is focusing on "care-receiving robots" to address societal issues like aging and loneliness. He highlights the importance of using robotics to enhance quality of life, particularly for the elderly. However, he cautions against the current hype surrounding humanoid robotics, warning that many advancements are still reliant on basic pattern-matching techniques. Pratt advocates for a balanced perspective to avoid potential disillusionment in the field, drawing parallels to the earlier challenges faced in automated driving.
Humanoid-robots Darpa Artificial-intelligence DrcRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
