Manipulation Learning

Manipulation learning focuses on enabling robots to perform dexterous tasks by learning from data, demonstrations, or simulations. Current research emphasizes improving the robustness and generalizability of manipulation skills through techniques like pre-training visual representations with interaction prediction, leveraging vision-based tactile sensing for deformable object handling, and employing reinforcement learning in dynamic, shared environments. These advancements are crucial for deploying robots in complex real-world scenarios, such as automated material handling, and for improving human-robot collaboration.