Multi Step Manipulation Task

Multi-step manipulation tasks challenge robots to perform complex actions requiring multiple sequential steps, aiming to improve robotic dexterity and adaptability in unstructured environments. Current research focuses on developing efficient planning algorithms, often employing hierarchical approaches that decompose complex tasks into simpler sub-tasks, and leveraging learning from demonstrations (LfD) or imitation learning to acquire complex manipulation skills. These advancements utilize various model architectures, including relational dynamics models, Gaussian splatting for scene representation, and neural networks for feasibility checking and action prediction, ultimately improving task success rates and planning efficiency. This research area is significant for advancing robotics capabilities in areas like household assistance and industrial automation.