Contact Implicit

Contact-implicit methods aim to generate robot trajectories that automatically handle contact events without pre-defining contact schedules, enabling more robust and versatile robot behaviors in complex environments. Current research focuses on improving the efficiency and scalability of algorithms like iterative Linear Quadratic Regulators (iLQR) and simultaneous trajectory optimization and contact selection (STOCS), often incorporating advanced contact models such as hydroelasticity. These advancements are significant for improving robot control in manipulation and locomotion tasks, particularly in scenarios involving many contacts or dynamic interactions, leading to more agile and adaptable robots.