Computationally efficient motion planning avoids exhaustive exploration of high-dimensional configuration spaces by leveraging the structure present in real-world planning problems. We argue that this can be accomplished most effectively by carefully balancing exploration and exploitation. Exploration seeks to understand configuration space, irrespective of the planning problem, while exploitation acts to solve the problem, given the available information obtained by exploration. At the chair for “Robotics and Embedded Systems” an Exploring/Exploiting Tree (EET) planner was developed that balances its exploration and exploitation behavior. The planner acquires workspace information and subsequently uses this information for exploitation in configuration space. If exploitation fails in difficult regions, the planner gradually shifts to its behavior towards exploration. We present experimental results to demonstrate that adaptive balancing of exploration and exploitation leads to significant performance improvements, compared to other state-of-the-art sampling-based planners.