Contact Planning

Contact planning in robotics focuses on efficiently generating robot motions that involve contact with the environment, crucial for tasks like legged locomotion and manipulation. Current research emphasizes developing fast, robust planning algorithms, often integrating machine learning models like neural networks (e.g., for contact feasibility classification and trajectory optimization) with established methods such as Monte Carlo Tree Search and Model Predictive Control. These advancements aim to enable robots to perform complex tasks in cluttered or constrained environments, improving dexterity and expanding their capabilities in real-world applications. The ultimate goal is to create more adaptable and versatile robots capable of handling a wider range of interactions.