Behavior Trees in Robotics (Part 1 - Concept)

Summary

This video delves into behavior trees, a critical tool in robotics and game engines, offering a solution to the limitations of finite state machines (FSMs). It explains the concept of behavior trees, their components, and how they address issues like reactivity and modularity. The video contrasts FSMs with behavior trees, highlighting the latter's scalability and adaptability for complex systems. It also previews upcoming content, which includes coding a behavior tree using the open-source library behavior.cpp in C++.

Takeaways

• 🌳 Behavior Trees are a method used in robotics and game engines to manage and structure tasks for autonomous agents.
• 🔄 Before Behavior Trees, Finite State Machines (FSMs) were predominant, but they had limitations that led to the development of Behavior Trees.
• 🤖 FSMs have a finite number of sequential states, which can become complex and hard to manage as the number of states and transitions increase.
• 🔄 The two main issues with FSMs are their lack of reactivity to unexpected events and poor modularity, making them less scalable for complex systems.
• 🌟 Behavior Trees offer improved reactivity and modularity, allowing for easier handling of complex scenarios and better code reuse.
• 📚 Behavior Trees consist of control flow nodes (like sequences, fallbacks, and decorators) and execution nodes (actions and conditions), which provide structure and functionality.
• 🔄 A Behavior Tree is executed from the root node through a process called 'ticking', which propagates down the tree based on node types and outcomes.
• 🔄 The video provides a detailed example of a robot's Behavior Tree for finding, grasping, and disposing of a ball, illustrating the tree's reactivity in action.
• 🔄 Behavior Trees are particularly useful in robotics for their ability to handle complex and dynamic environments more effectively than FSMs.
• 🛠️ The video series will proceed to code a Behavior Tree from scratch using an open-source library called behavior.cpp, demonstrating practical implementation.

Q & A

What is the main topic of the video?

The main topic of the video is to explain what Behavior Trees are, their origin, and the advantages of using them in robotics and game engines.

Why were Behavior Trees introduced?

Behavior Trees were introduced to address the limitations of Finite State Machines (FSMs), particularly in terms of reactivity and modularity, which FSMs struggled with as systems became more complex.

What is a Finite State Machine (FSM)?

A Finite State Machine (FSM) is an abstract machine with a finite number of sequential states. It transitions between these states based on inputs and the current state.

What are the two main problems with FSMs?

The two main problems with FSMs are reactivity and modularity. FSMs are not reactive enough to handle unexpected situations or corner cases, and they lack modularity, making it difficult to reuse states in different contexts without modifications.

How does a Behavior Tree differ from an FSM in terms of reactivity?

Behavior Trees are more reactive than FSMs because they can handle unexpected situations by continuously ticking all nodes to the left of the currently executing node, allowing for dynamic responses to changes in the environment.

What are the basic building blocks of a Behavior Tree?

The basic building blocks of a Behavior Tree are internal nodes called control flow nodes and leaf nodes called execution nodes. Control flow nodes include sequence, fallback, parallel, and decorator nodes, while execution nodes include action and condition nodes.

What is a 'tick' in the context of Behavior Trees?

A 'tick' in Behavior Trees is a signal or nudge that starts the execution from the root node and is propagated through the tree, allowing nodes to execute if they are in the active state.

How does modularity in Behavior Trees solve the problem faced by FSMs?

Modularity in Behavior Trees allows for the reuse of subtrees independently of the rest of the tree, as each subtree is decoupled from others. This makes it easier to reuse and modify behavior without affecting other parts of the system.

What is the role of a sequence node in a Behavior Tree?

A sequence node in a Behavior Tree ticks its children from left to right and requires all children to return success for the sequence node to be successful. If any child returns running or failure, the sequence node will return the same status.

What is the role of a fallback node in a Behavior Tree?

A fallback node in a Behavior Tree ticks its children from left to right and will return success or running as soon as one child returns either of these statuses. If all children return failure, the fallback node will also return failure.

What is the difference between an action node and a condition node in a Behavior Tree?

An action node in a Behavior Tree is responsible for executing an action and returns success, failure, or running based on the action's outcome. A condition node checks for a condition and returns success or failure based on whether the condition is met or not.

Outlines

00:00

🌳 Introduction to Behavior Trees and FSMs

This paragraph introduces the concept of behavior trees and their significance in robotics and game engines. It contrasts behavior trees with finite state machines (FSMs), which were prevalent before behavior trees. The video aims to explain the evolution from FSMs to behavior trees due to limitations in reactivity and modularity. FSMs are described as having a finite number of sequential states, and their simplicity is demonstrated using the example of a computer's on/off state. The paragraph also sets the stage for a deeper exploration of behavior trees in subsequent videos, including coding examples in C++.

05:02

🤖 Challenges with Finite State Machines

The second paragraph delves into the problems associated with finite state machines (FSMs), particularly their lack of reactivity and modularity. It uses a robot example to illustrate how FSMs struggle with unexpected scenarios, such as an external agent disrupting the robot's task. The paragraph explains how increasing complexity in FSMs leads to a proliferation of states and corner cases, making them difficult to manage and debug. It also discusses the issue of modularity, where states in FSMs are not decoupled, leading to code that is hard to reuse without modifications. These challenges highlight the need for an alternative approach, which behavior trees address.

10:03

🔄 Understanding Behavior Trees and Their Components

The final paragraph provides an in-depth look at behavior trees, their components, and how they overcome the limitations of finite state machines. It describes behavior trees as directed rooted trees with control flow nodes and execution nodes. The paragraph explains the different types of control flow nodes: sequence, fallback, parallel, and decorator nodes, each with specific behaviors. It also differentiates between action and condition nodes. The paragraph uses the robot example to demonstrate how a behavior tree operates, emphasizing its reactivity and modularity. It shows how behavior trees can dynamically adapt to changes and how subtrees can be reused across different scenarios. The paragraph concludes by acknowledging the scalability of behavior trees for complex systems and预告即将在下一个视频中使用开源库behavior.cpp来构建行为树的实践操作。

Keywords

💡Behavior Trees

Behavior Trees are a data structure used in programming, particularly in robotics and game development, to manage and structure tasks or behaviors in an autonomous agent. They are designed to be more flexible and reactive than Finite State Machines (FSMs). In the video, Behavior Trees are introduced as a solution to the limitations of FSMs, providing a way to handle complex scenarios with greater modularity and reactivity. The video explains that Behavior Trees are composed of nodes that can be executed based on certain conditions, allowing for dynamic responses to changes in the environment.

💡Finite State Machines (FSMs)

Finite State Machines are a computational model that can be in one of a finite number of states at any given time. They are used to design systems where the next state is a function of the current state and the input. In the video, FSMs are discussed as the predecessor to Behavior Trees. They were widely used in robotics and game engines but faced challenges with reactivity and modularity, leading to the development of Behavior Trees. The script uses the example of a robot finding and picking up a ball to illustrate how FSMs work and their limitations.

💡Reactivity

Reactivity in the context of the video refers to the ability of a system to respond to changes in its environment or inputs. Behavior Trees are highlighted as being more reactive than FSMs, allowing for more dynamic and flexible responses to external events. The video contrasts this with FSMs, which may not be able to handle unexpected changes as effectively, requiring the system to be made more complex to account for these scenarios.

💡Modularity

Modularity in software design refers to the concept of creating independent, interchangeable modules that can be easily reused in different parts of a program. The video emphasizes that Behavior Trees offer better modularity than FSMs, allowing developers to reuse subtrees or nodes without being dependent on their context within the tree. This makes Behavior Trees more scalable and easier to manage in complex systems.

💡Control Flow Nodes

Control Flow Nodes are internal nodes in a Behavior Tree that dictate the order in which execution nodes are processed. The video explains that there are four types of control flow nodes: Sequence, Fallback, Parallel, and Decorators. These nodes manage the flow of execution within the tree, determining how the system responds to different conditions and inputs.

💡Execution Nodes

Execution Nodes are the leaf nodes in a Behavior Tree that represent actions to be performed or conditions to be checked. They are the terminal points in the tree where actual tasks are executed or evaluated. The video distinguishes between two types of execution nodes: Action nodes, which perform tasks, and Condition nodes, which check for specific states or conditions.

💡Sequence Node

A Sequence Node is a type of control flow node in a Behavior Tree that processes its children from left to right until one of them returns a failure or running status. If all children return success, then the sequence node also returns success. The video uses this concept to explain how a sequence node ensures that a series of actions must all be completed successfully for the overall task to be considered successful.

💡Fallback Node

A Fallback Node is another type of control flow node that tries its children from left to right and will return success or running as soon as one of the children does so. If all children return failure, then the fallback node also returns failure. The video script uses the example of a robot's behavior to illustrate how a fallback node can handle different scenarios, such as finding and then picking up a ball, by trying different actions until one succeeds.

💡Parallel Node

A Parallel Node is a control flow node that executes all its children simultaneously and evaluates their success or failure to determine its own status. The video mentions that a parallel node returns success if a specified number of its children succeed, failure if a certain number fail, and running otherwise. This node type is useful for managing concurrent tasks in a system.

💡Decorator Node

A Decorator Node is a control flow node that adds additional functionality to a child node without changing its core behavior. The video script explains that decorators can be used to modify the behavior of a node, such as allowing it to fail a certain number of times before the failure is propagated. This enhances the flexibility of the Behavior Tree by allowing for more complex conditional behaviors.

Highlights

Behavior trees are introduced as a solution to the limitations of finite state machines (FSMs) in robotics and game engines.

FSMs have a finite number of sequential states, which can become complex and hard to manage in dynamic environments.

Behavior trees offer improved reactivity and modularity over FSMs, making them more suitable for complex systems.

The video explains the concept of behavior trees through a simple example of a robot finding and interacting with a ball.

A behavior tree is a directed rooted tree with internal nodes called control flow nodes and leaf nodes called execution nodes.

Control flow nodes include sequence, fallback, parallel, and decorator nodes, each with a specific function within the tree.

Execution nodes are of two types: action nodes that perform tasks and condition nodes that check for specific conditions.

The video demonstrates how a behavior tree can handle unexpected situations, such as an external agent interfering with the robot's actions.

Behavior trees are shown to be more scalable for complicated scenarios due to their inherent reactivity and modularity.

The video discusses the practical problems with FSMs, such as lack of reactivity and modularity, which led to the development of behavior trees.

The concept of a 'tick' in behavior trees is introduced as a method to initiate and propagate execution through the tree.

The video provides a detailed breakdown of the behavior tree structure, explaining the roles of sequence and fallback nodes.

Decorator nodes are highlighted as a way to add functionality to child nodes, such as allowing multiple failures before a task is considered failed.

The video concludes with a预告 of the next video, where the audience will learn to code a behavior tree from scratch using the behavior.cpp library.

The video recommends a book for further reading on behavior trees, indicating that there is more depth to the subject than covered in the video.

The video emphasizes the practical applications of behavior trees in robotics and game engines, highlighting their widespread use.

Transcripts

behaviors this video is about us

understanding what Behavior trees are

how they came into being and what are

the advantages of using Behavior trees

in robotics this series will start with

the conceptual idea of behavior trees in

this video and in the next videos of

this series we will actually start

coding in C plus to build a behavior

tree okay enough about the series let's

come back to the idea of behaviors to

understand Behavior trees let's go back

to a time where we did not have Behavior

trees but fsms were the main thing in

robotics and game engines let's also

understand what behaviories and fsms are

in general for they are ways to

structure and manage the switching

between different tasks in an autonomous

agent which could be inside a game like

a virtual entity or a robot but let's

talk about fsm's first and that will

lead to certain problems and then that

will lead to our discussion about

Behavior trees and how this started what

exactly is a finite State machine or as

we said FSM and FSM or finite State

machine is an abstract machine with

finite number of sequential States

this sounds very boring and full of

jargons so let's just simplify this

using an example okay now imagine a

machine for example your computer in a

very simple scenario your computer can

be in two states on or off so this will

be a state machine for that and this has

two states so this is a finite State

machine here one state depends on the

input which is a button press and in

this case we'll simplify it to a toggle

and then also it depends on the previous

date so that's why it's sequential so it

depends on the previous state and the

input given now this was a very simple

example of a finite State machine you

can of course go into the details of

finite State machines because what I've

explained is not actually exhaustive

it's a very simple watered down idea of

a finite State machine now I hope you've

either understood the idea of a finite

State machine in simple terms or you've

researched about finite State machines

already in the past so let's look at a

more evolved but still very simple

finite State machine of a robot this

robot is supposed to find a ball go to

the ball pick it up then go to a bin and

drop it in the bin and the process

repeats this is the state machine of

that robot spend a couple of seconds

understanding what the state machine is

as I said the state machine has finite

States and it is sequential that means

that the current state depends on the

previous state and the input the input

is given through sensors and the

previous state is of course the previous

state for instance let's say the first

date is finding the ball if the ball is

found we go to the next state to

approach the ball if we approach the

ball we go to the next state which is

grasping the ball and then we go to the

bin and then we drop the ball and then

the process repeats again but during

this process if there is a fault

somewhere then the system should ask for

help so this is our state machine for

the simple robot now we were talking

about finite State machines and you

might get impatient and then you'd ask

why are we talking about this well

before Behavior trees finite State

machines were ruling the world of game

engines and Robotics but it had a couple

of problems which led to the creation

and usage of behavior trees looking at

this FSM we are happy because it seems

to work well but there are two problems

with this state machine all this finite

State machine one reactivity and second

modularity let's start talk about them

individually now first up reactivity now

the problem with the state machine is

that let's say an external agent comes

when our robot is supposed to drop the

ball to the bin and it pushes the ball

out of the robot's hand what will the

state machine do in that case it assumes

that nothing like that will happen what

we need to do here is that we need to

make our state machine more complicated

for complicated scenarios right so we

can do that we can add an additional

state or we can upgrade the state which

is placeball so that it constantly

checks if the ball is still there that's

still fine but in a real scenario there

are so many complexities as you increase

the complexity you would need to have

more States that's still okay but you

would need to handle each state with a

lot more intelligence and then one state

might not lead to another state but it

might lead to five or six or n different

states depending on how complicated the

system is so we can take one of the

paths now as we make the system more

complex due to requirements making a

finite State machine becomes super hard

because you will have so many Corner

cases you will have so many issues so

the problem is a practical finite State

machine which is understandable for us

and we can debug it properly is not

reactive enough and of course the more

Corner cases you have the more

intelligence you want to put in a finite

State machine the more complicated it'll

get and we as Engineers might not be

able to deal with it so in simple terms

you might make a mess of your state

machine if you keep using finite State

machines for complicated scenario a

practical State machine is not reactive

enough to react to these Corner cases

and complexities in the system second

modularity now one state here in this

finite State machine is actually not

decoupled from the previous state and

the next state for instance cross ball

is supposed to be connected to approach

ball and approach bin in this case

what's happening is that if for some

reason you want to use this state again

because the system is getting more

complex and you want to grasp ball not

just to approach the bin and put it the

bin but for something else as well you

cannot use it as it is because it is not

modular it will have code which at least

indirectly depends on the previous state

and the next date so that means if we

want to use the state or the code inside

the state again we cannot do that we

will have to make changes to this code

to check if the next state is this or

that if the previous state is this or

that and as I said before as these

things happen the system becomes more

complicated it just becomes a mess and

thus a state in itself is not modular

these are the two main problems with a

finite State machine and a finite State

machine starts failing practically for

development and deployment once the

system starts becoming more complicated

it's great to use a finite State machine

if the system is very simple and then if

your environment is constrained and

predictable but because of the previous

two issues we can say that in general as

the system becomes more complex a finite

State machine is not scalable so that is

the problem which led to the creation of

behavior so now after all the story we

finally talking about Behavior trees

this is the corresponding behavior of

your state machine it might look

slightly random if you don't know what's

actually happening so what we'll do is

we will understand different components

which together form a behavior tree

we'll also check if this behavior is

doing what we want but with reactivity

and modularity right because these were

the two reasons which led to the

creation of behaviorly so let's start

with understanding the basic building

blocks of behavior trees and then we'll

come back to this Behavior tree and see

what's actually happening a behavior

tree is a directed rooted tree which has

internal nodes called control flow nodes

and leaf nodes called execution nodes a

behavior restarts its execution from the

root mode using something called a tick

a tick is basically like a nudge to a

node where it'll start functioning so

the root node actually starts the stick

or this nudge and it nudges all the

nodes just below that so all the

children of this root node and this

nudge or a tick is gradually propagated

a node is only executed if it is State

otherwise it's not also taking in

Behavior tree happens from left to right

so depending on what the parent node is

it will either take only the left node

or all the nodes but we'll get into that

in a bit in general there exists four

different categories of control flow

nodes sequence fallback parallel and

decorators a sequence node is a control

node with children where it actually

starts ticking from the left node and

then it gradually moves towards the

right but the catch here is that a

sequence node takes its children

starting from left to right until it

finds a child node which returns either

running or failure so sequence node

actually requires all the children to

return success for it to say that hey I

am successful and when that happens it

will return success to its period note

that when a child of a sequence node

returns running of failure then the next

node or the next child node to the one

which return this is not ticked so it is

only take if the previous one returns

success a fallback node on the other

hand routes the this text to its

children from left to right until it

finds a node or a child node which

returns either success or running if all

the child nodes return failure only then

is this note saying hey I failed

otherwise it will return a success or

running as soon as one of its children

returns running all success know that

for a fallback node if a child returns

running or success the next node is not

date so the status is returned back to

the parent node which is the fallback

node and this fallback node reports back

to its parents saying running all

success a parallel node routes sticks to

all its children and returns success if

M of these nodes actually return success

it returns failure if n minus M plus 1

notes where n is the total number of

nodes return failure and otherwise it

returns running a decorator node is an

added functionality on a child for

instance if you want to allow the child

node to fail n number of times then you

can add a decorator node to basically

enhance its functionality and say Hey

you can fail n number of times now

talking about execution notes there are

two simple types of execution notes one

is action and one is condition action

node is responsible for carrying out an

action and then it returns either

success failure or running and a

condition node checks for a condition

and returns success or failure based on

that condition passing or failing now

let's combine all of this and look back

at our example where the robot was

supposed to find a ball and then finally

drop the ball in a bin and the process

repeats looking at this Behavior tree

the root note is a fallback node that

means it goes to the left side of the

tree which is our main tree and the

right side is asked for help if the left

side returns running or success then the

right side is not used at all right so

the right child which is asked for help

is not used if the left tree returns

success that is everything is done the

ball was finally dropped in the bin or

it returns running which means the root

node has to wait anyway now going down

the three by one the next node is a

sequence node that means it wants to

tick all the children one by one and

when all of them return success only

then it's happy and it returns success

back to its paid node which is the root

node if not it will either return

running based on what the children are

doing if one of the child returns

running it will return running back

otherwise if one of the child returns

failure then it will return failure and

then we'll go to ask for help now let's

go to the next level we see this level

filled with fallback notes again that

means this fallback node will first take

its left child and then if the left

child fails only then it will go to the

right child let's just take one example

because it's just repetitive and it's

the same for all of these fallback notes

now let's take the extreme left side of

this here what you have is a fallback

node which has two children one ball

found condition node and second fine

Ball action node ball found condition

node just checks if the ball is actually

found or not if it's found then it

returns success that means this part of

the tree returns success to its parent

which is a sequence node so it will

basically take the next one but if ball

is not found then this fallback node

will look at the next child which is

fine ball if fine ball returns running

then this node will return running back

to its parent node that means the system

has to wait until this running is

changed to either success or failure so

this node constantly takes ball found

and the action node which is fine ball

and then once the ball is actually found

this will eventually return success

right because the condition ball found

becomes true so ball found returns

success so once this is done the

sequence node which was the parent of

all these fallback nodes takes the next

fallback node which is about approaching

the ball the same logic is applied there

and hence all the fallback nodes from

left to right are ticked one by one now

one thing to consider here is that this

is a reactive Behavior tree which means

that all the nodes to the left of the

currently take note are also ticked what

does that mean in this case and why did

I say that this is a reactive Behavior

tree let's say in this case the ball is

grasped and then the robot is moving

towards the bin somehow an external

agent pushes the ball away from the hand

of the robot now in this case since all

the nodes which are on the left to the

node we're talking about which is for

approaching the bin since all the left

fallback nodes were ticked continuously

as soon as the ball slips away all is

visible but the ball is not close to the

robot so the second fallback node comes

into play here now the robot will again

approach the ball so in this case the

behavior tree is already pretty reactive

that was not the case with a finite

State machine if something like that

happened for a finite State machine it

would have failed so we would have to

make changes to that finite State

machine to consider this example or this

corner case but then you have so many

Corner cases so this is one example

where reactivity is clearly there that's

why I was saying a behavior tree is

reactive but a finite State machine is

not talking about the second Advantage

now modularity one module or one subtree

of this Behavior tree is actually

independent of all the other it doesn't

know where the take is coming from it

doesn't know which sub tree will stick

before so you can actually reuse this

again and again and let's say you have

to grasp the ball again and again in

different scenarios you can use the same

subtree from the behavior tree instead

of doing something else making some

other changes is because subtree is

decoupled from all other subfrees or

different parts of the Trees of this

Behavior tree so that solves our problem

of modularity now because of reactivity

and modularity the idea of behavior tree

is very scalable for complicated

scenarios and complicated systems that

is exactly why Behavior trees came into

being and that is why they are used so

much in robotics and game engines now so

this was the basic idea of behavior

trees I hope you understood what a

behavior tree is how they came into

being because of problems with finite

State machines and what are the

advantages there is a lot more to

understand when it comes to behavior

trees I'm adding a link to the book I

read which is all about Behavior trees

it is a brilliant book this video just

covers the basics and there is a lot

more to understand now in the next video

we will code a behavior tree from

scratch for a simple example using

something called behavior.cpp which is

an open source library for making

Behavior trees this is a brilliant

brilliant Library made by Davide and I

absolutely love this Library so we're

gonna talk about that Library we will

start making a simple Behavior tree

using that library in C plus plus and

then we'll take it from there and then

gradually build upon that to make

robotic Behavior thank you for watching

this video I'd love to know what you

think about this and I will see you in

the next video bye