Researchers are making monumental strides in enhancing robotic dexterity and tactile sensing. The goal? Robots that can manipulate objects with the finesse and precision of human hands. At the forefront of this research field is a groundbreaking study from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). The team tackled the intricate challenge of contact-rich manipulation, a domain where robots interact with objects in complex ways. “The main challenge for planning through contact is the hybrid nature of contact dynamics,” the study notes. Reinforcement learning is a technique used by AI to train a model based on rewards and punishments. The researchers here used a type of reinforcement learning method called “smoothing” to simplify the way living beings go through the process of sensing things and make it replicable by a primitive robot. What’s more, their method, combined with sampling-based motion planning, paves the way for more intricate manipulation involving numerous contact points. In other words: Using two hands to manipulate and interact with an object. Their experiments have showcased the ability to generate intricate movements in mere minutes, a significant leap from the hours demanded by traditional RL methods.
Full story : AI Researchers Are Teaching Robots to Mimic Human Dexterity.
