IntroductionWhen passing a crowd to get where they want to go, people usually travel the distance safely without worrying about what they are doing. They learn from the actions of others and take note of obstacles to avoid. On the other hand, robots, unlike humans, have difficulty handling such navigation concepts. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Motion planning algorithms will generate a tree of possible decisions that branches until it locates good paths for navigation. A robot that needs to cross a room to reach a door, for example, will need to produce a step-by-step search tree of possible movements and then decide the best path to the door, taking into account various obstacles. A drawback is that these algorithms rarely learn: robots cannot exploit information about how they or other agents have acted before in similar environments. Andrei Barbu, one of the researchers and affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL), compared the robots' situation to playing a game of chess. Decision trees will branch until the robots find the optimal way to navigate. But unlike chess players, robots will explore what the future will look like without learning much about their environment and other agents, according to Barbu. It's always complicated for robots, whether they're going through the same crowd for the first time or for the thousandth time. They will always explore, but rarely observe and never use what happened in the past, Barbu said. MIT researchers fused a motion planning algorithm with a neural network, which then learns to recognize paths that might lead to the best results and uses that information to guide the robot's movement in a certain environment. They proved the effectiveness of their model in two scenarios: navigating rooms filled with traps and narrow passages, and navigating areas while avoiding collisions with other agents. Yen-Ling Kuo, Barbu's research colleague and doctoral student at CSAIL, said the aim of their research is to incorporate into the search space a new machine learning model that knows how to make planning more efficient based on past experience. Existing motion planning algorithms explore an environment by rapidly growing a decision tree that ultimately spans an entire space. The robot then looks at the tree to find a way to reach its goal, such as a door. On the other hand, the model designed by the researchers offers a compromise between exploring the environment and using past experiences, Kuo says. Teaching robots to navigateThe learning process begins with a few examples. A robot using the model is trained to navigate similar environments in multiple ways. The neural network learns what makes these examples successful by interpreting the environment around the robot, such as the shape of the walls, the actions of other agents, and the characteristics of the goals. In short, the model learns that when it's stuck in an environment and it sees a door, it will think it's probably a good idea to go through the door to get out, Barbu pointed out. The model unifies the exploration behavior of previous methods with this learned information. The motion planner, called RRT*, was created by MIT professors Sertac Karaman and Emilio Frazzoli. It is derived from a.