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Four Ways Computer Vision Is Driving AI-Enhanced Robotics

Artificial intelligence (AI)-enhanced robotics has emerged as a primary area of growth in the field of computer vision. As the IEEE CS 2025 Technology Predictions report explains: “Embodied intelligence will enable robots to perceive, learn, and collaborate in dynamic environments, achieving unprecedented autonomy and human-like adaptability.” Market research supports that sentiment: According to Statista, the AI robotics market size is expected to show an annual growth rate (CAGR 2025-2030) of 23.37%, resulting in a market volume of US$64.35bn by 2030.

Developments with large language models (LLMs), multimodal AI, and broader computer vision efforts have been among the primary forces behind this rapid acceleration, and as the industry gears up for its leading AI engineering event, the Computer Vision and Pattern Recognition Conference (CVPR), paper submissions reiterate the role that AI-enhanced robotics will play.

“A lot of people in computer vision are now interested in robotics,” said Phillip Isola, CVPR 2025 Program Co-Chair and an associate professor at the Massachusetts Institute of Technology (MIT) in Boston, Mass., U.S. “They're first starting by modeling 3D scenes, and that will be more relevant to robotics. For example, they might be working on navigation of a house, and there's no robot. But that's where they're going.”

CVPR 2025 Program Co-Chair Fuxin Li, an associate professor at Oregon State University in Corvallis, Ore., U.S., agreed, adding that the program has evolved accordingly. “It’s an emerging trend. We see more and more convergence between the two areas. There are also a lot more real robots in CVPR papers than a few years ago.”

Current research focus areas

So, how exactly is this emerging trend shaping today’s computer vision and pattern recognition landscape? From real-world, 3D data guiding robots in their tasks to datasets for navigating spatial relations to other datasets designed to maximize a robot’s functional abilities to pick up, move, or shift elements in an environment, much of the work in the intersection of computer vision and robotics focuses on ways to train robot understanding and ensure they function efficiently and successfully in a variety of tasks. Specifically, current research focus includes:

  1. Expanding robotic intelligence. To harness the full potential of robotic automation in a wide variety of landscapes, robots need to be able to address not just menial tasks but elevate their “thinking” to a more challenging cognitive level. In the CVPR paper “RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete,” researchers describe a dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. 
  2. Addressing robotic dexterity and movement. While higher-level tasks require superior thinking, more physical tasks require advanced precision. Thus, researchers are aiming efforts on angle manipulation, grip, and more. For example, “3D-MVP: 3D Multiview Pretraining for Robotic Manipulation,” a paper to be presented at CVPR, will demonstrate a multi-view transformer to understand the 3D scene and predict gripper pose actions. Another paper, “Let Humanoid Robots Go Hiking! Integrative Skill Development over Complex Trails,” seeks to train robots as hikers on complex trails via a universal learning framework that strives to address both visual perceptual awareness and body dynamics.
  3. Applying robotic automation to industrial and business environments. Research efforts focus not only on the developmental side of robotics and AI but also on addressing their more rapid deployment for routine tasks. The CVPR Workshop on Perception for Industrial Robotics Automation will focus on a “bin picking” challenge, where robots are tasked with grabbing a randomized selection of objects from a bin at different angles/clasps. The competition awards up to USD$60,000 in prizes. In addition, CVPR exhibitor Fourier will be demonstrating its GR-1 robots, which according to its website, are being developed to be the next generation of bank reception manager, factory support, and rehabilitation diagnosis, training, and treatment.
  4. Identifying robotic opportunities in consumer functions. Consumer applications offer a large area of potential, bringing forth visions of the housekeeper, Rosie, from the cartoon The Jetsons and far beyond. For instance, the CVPR paper, “VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation,” uses in-the-wild human videos to train robots on tasks such as closing the fridge, opening the cupboard, wiping the counter, and much more. Building on these concepts, exhibitor Booster Robotics has developed humanoid robots that play soccer—and offers a framework for training learning-based locomotion from the ground up.

While much work remains in the field of AI-enhanced robotics, just as much opportunity awaits. According to analysts, “The growing investment in research and development to enhance the capabilities of AI robotics is expected to propel market growth in the coming years.” From the implications in the industries ranging from healthcare and manufacturing to consumers and Smart Cities, the work in computer vision and pattern recognition today will have lasting influence on the future of robotics and its positive impact on humankind.

For more information on CVPR 2025, taking place 11-15 June in Nashville, Tenn., U.S., or to register, visit https://cvpr.thecvf.com/.