A New Twist on Old Joints: How Robots Learn to Open Drawers
A novel robotic method fuses deep learning with classical screw theory to enable service robots to manipulate common articulated objects like drawers and refrigerators. The system begins with a visual prediction of an object’s articulation model before contact, then refines its estimate in real-time using kinematic and force data during manipulation. This hybrid approach, evaluated in simulation and real-world hardware, addresses a core challenge in domestic automation: how to interact with an object before fully understanding its mechanics.
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