Long Horizon Manipulation

Long-horizon manipulation in robotics focuses on enabling robots to perform complex tasks requiring sequences of actions over extended timeframes, such as tying a tie or assembling furniture. Current research emphasizes developing robust reward models, hierarchical task decomposition methods (often leveraging visual information and pre-trained models), and efficient imitation learning techniques to overcome the challenges of high dimensionality and complex dynamics inherent in these tasks. These advancements are crucial for creating more versatile and adaptable robots capable of assisting humans in a wider range of real-world scenarios, impacting fields like assistive robotics and automated manufacturing.