Behavior trees can help design robots that are highly adaptive and reactive to their environment.
A behavior tree is a way to design the switching between multiple tasks in autonomous devices such as a robot. It consists of behaviors or nodes connected to each other in the form of a tree, starting with a single root node. Behavior trees can help in creating an efficient method of designing complex algorithms and systems that are both modular and reactive. Modularity means the degree to which the components of a system can be separated into individual blocks and then recombined. Reactivity implies the ability of an algorithm to react to changes efficiently. Behavioral trees can be designed, tested, and implemented one module at a time. They can also be highly responsive to the environment around them. It is because of these features that behavior trees will play an important role in robotics and AI development and have an advantage over finite-state machines. A finite state machine consists of a set of predefined states and a set of transitions between these pairs of states. Thus, they tend to be unusable in complex systems that depend on the environment to carry out actions. FSMs are highly susceptible to human errors, hard to modify with increasing transitions, and are impractical to reuse. Thus, FSMs become limited in designing a complex robotics system, and this is where behavior trees step in.
The Role of Behavior Trees in Robotics and Artificial Intelligence
Imagine a robot employed in a warehouse tasked with the duty to place objects from the assembly line onto particular shelves. An FSM would take the object and then start walking towards the shelves. It won’t pause or stop if an employee comes in its path or if the object falls down from the robot’s grip. The robot continues with the task even though the end result will remain incomplete. This results not only in the loss of productivity for that instance but also can cause a butterfly effect and result in more inefficiencies.
The same robot designed with behavior trees is highly modular and reactive. The actions of the robot are carried out or aborted according to ticks, which depend on the leaf node’s return statuses. In simpler words, if the robot faces an obstacle in its path, it stops with the assigned task and returns to a safe position. A safe position, in this case, can be waiting for the human to clear the path or returning back to the assembly line and alerting the employee about the incident. Thus, behavioral trees can be used to design a highly responsive robotics system. Therefore, robots programmed with behavior trees prove highly beneficial as cobots as they can react and respond effectively to their environment.
Behavior trees have proven beneficial in training AI and robotics. Industry leaders attribute modularity as the main reason for the prominence of behavior trees in AI and robotics. Modularity is a key enabler for designing complex and reusable systems that are currently being used in AI and robots. Thus, behavior trees have gained the confidence of industry leaders in robotics and AI. Michele Colledanchise and Petter Ogren in their book, Behavior Trees in Robotics and AI: An Introduction wrote, “There are a lot of different ways to create AI’s, and I feel like I’ve tried pretty much all of them at one point or another, but ever since I started using behavior trees, I wouldn’t want to do it any other way. I wish I could go back in time with this information and do some things differently.”