Sampling Based

Sampling-based methods are widely used to address computationally challenging problems in robotics and optimization, primarily aiming to efficiently find near-optimal solutions within vast search spaces. Current research focuses on improving the speed and scalability of these methods through techniques like parallel processing (e.g., GPU acceleration), adaptive sampling strategies (e.g., informed trees, context-generative policies), and integration with other approaches such as gradient-based optimization and search algorithms. These advancements are significantly impacting fields like motion planning, where real-time performance is crucial, and large-scale stochastic programming, where exhaustive enumeration is infeasible, leading to more efficient and robust algorithms for various applications.