OK-Robot system developed by researchers from NYU and Meta can train robots to pick up and move objects in new settings utilizing an open-source AI object detection model. Testing in homes, the robot successfully completed tasks in 58.5% of cases, rising to 82% in less cluttered rooms. The use of open-source AI models presents both advantages and limitations.
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Training Robots to Navigate Unfamiliar Environments
Robots are becoming increasingly adept at certain tasks, such as picking up and moving objects, and even cooking. However, deploying them in new and unfamiliar settings poses a significant challenge.
Introducing OK-Robot System
A new system called OK-Robot has been developed to train robots to operate in previously unencountered environments, bridging the gap between advanced AI models and real-world robot capabilities without the need for complex and costly training.
How OK-Robot Works
Researchers from New York University and Meta tested the system with a commercially available robot named Stretch in various homes. Using Record3D, an iPhone app, they scanned the surroundings and ran an open-source AI object detection model over the video frames to identify objects and locations in the room.
Success Rate and Limitations
The robot successfully picked up and moved specific items in 58.5% of cases, with a higher success rate in less cluttered rooms. However, when the robot couldn’t find the instructed object, it stopped instead of trying to find a solution, highlighting the limitations of the system in cluttered environments.
Combining OK-Robot with Voice Recognition
Researchers believe that combining OK-Robot with voice recognition models could enable simple instruction delivery to the robot, potentially expanding its capabilities beyond object manipulation.
AI Solutions for Middle Managers
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Connect with Us
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