Task Driven Manipulation

Task-driven manipulation focuses on enabling robots to perform complex manipulation tasks, such as grasping, reorienting, and shaping objects, based on the desired outcome rather than pre-programmed motions. Current research emphasizes developing robust methods for handling uncertainty, including using generative adversarial networks (GANs) for grasp planning, hierarchical planning for multi-object manipulation, and Bayesian optimization with tactile feedback for in-hand manipulation of unknown objects. These advancements are crucial for expanding the capabilities of robots in diverse environments, particularly in applications requiring dexterity and adaptability, such as space exploration and assistive robotics.