Contact Selection

Contact selection, the process of determining optimal contact points and forces between interacting objects, is crucial for tasks ranging from robotic manipulation and assembly to human-robot collaboration and even customer service routing. Current research focuses on developing efficient algorithms, such as simultaneous trajectory optimization and contact selection (STOCS) and deep learning models, to predict and manage contact interactions, often incorporating techniques like mathematical programming and sampling-based planning. These advancements improve the robustness and efficiency of complex systems by enabling more accurate prediction of contact complexity, leading to better task planning and execution in robotics and more effective resource allocation in service industries.