When moving through a crowd to reach some goal, humans can usually navigate the space safely without thinking too much. They can learn from the behavior of others and note any obstacles to avoid. Robots, on the other hand, struggle with such navigational concepts. MIT researchers have now, however, devised a way to help robots navigate environments more like humans do. The new technique relies on setting a motion planning model that lets robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they have learned before in similar situations, the German news agency reported. Popular "motion-planning algorithms" will create a "tree" of possible decisions that branches out until it finds good paths for navigation. A robot that needs to navigate a room to reach a door, for instance, will create a step-by-step search tree of possible movements and then follow the best path to the door. The Phys.org website cited Andrei Barbu, a researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), as saying: "Just like when playing chess, these decisions branch out until the robots find a good way to navigate." Yen-Ling Kuo, who took part in the trial, said: "The idea behind this work is to add to the search space a machine-learning model that knows from past experience how to make planning more efficient."