Vision-Based Tracking! Robots Can 'Outsmart' in Hide-and-Seek Games


On February 25, 2026, Google announced that it would incorporate Intrinsic, a robotics software subsidiary of Alphabet, into its operations. The move aims to integrate its top AI models and infrastructure, accelerating the adoption of robotics technology in the industrial manufacturing sector. Intrinsic will continue to operate independently but will shift its research focus towards deep collaboration with Google DeepMind, fully integrating the Gemini AI model and Google Cloud services to advance robotics technology.
Arm announced the establishment of a 'Physical Artificial Intelligence' department at CES, integrating robotics technology with automotive business to expand the robot market. The company believes there are commonalities between the two in terms of sensors and hardware, and will focus on meeting customers' demands for power consumption and performance.
Recently, Figure founder and CEO Brett Adcock announced a new machine learning model called Helix, aimed at enhancing the capabilities of humanoid robots in household environments. This news comes just two weeks after Figure announced the end of its collaboration with OpenAI, demonstrating their strong commitment to the field of robotics technology. Helix is a 'general-purpose' visual-language-action (VLA) model capable of processing visual data.
Recently, OpenAI submitted a new application to the trademark office indicating that the company may be entering the humanoid robotics field. According to the application filed on January 31, OpenAI plans to launch several new products, notably mentioning 'user-programmable humanoid robots' and robots capable of communication and learning, aimed at providing assistance and entertainment for people. Moreover, OpenAI is actively recruiting new members for its robotics team. According to the company’s official recruitment information, they currently have openings for mechanical product engineers, robotics specialists, and senior researchers.
Recently, a study from the Swiss École Polytechnique Fédérale de Lausanne (EPFL) made its debut at the IEEE International Conference on Robotics and Automation in Rotterdam. This research aims to explore how robotic hands can break existing limitations to grasp more objects. The research team noted that deep learning models significantly enhanced the dexterous manipulation capabilities of multi-fingered hands, but the grasping guided by contact information in cluttered environments has not yet been fully explored. To address this issue, the researchers designed an inspired hand that can bend backward to pick up various objects and detach itself to climb.