Structered Deep Visual Models For Robot Manipulation

In order to enhance robot manipulation tasks and motion planning, a unique layered architecture that includes visual foundation models is presented in this research. Five levels make up the framework: learning, perception, cognition, planning, and execution. We improve the robot's perception of its surroundings through the use of visual foundation models, allowing for more precise motion planning and effective task comprehension. This method enables ongoing learning and real-time modifications, which greatly enhances job execution. The outcomes of the experiments show how well the suggested framework works in a range of robot manipulation tasks and motion planning situations, underscoring its potential for real-world implementation in dynamic contexts.