Behavior Trees in Robotics (Part 1 - Concept)
Summary
TLDRThis 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
🌳 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++.
🤖 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.
🔄 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。
Mindmap
Keywords
💡Behavior Trees
💡Finite State Machines (FSMs)
💡Reactivity
💡Modularity
💡Control Flow Nodes
💡Execution Nodes
💡Sequence Node
💡Fallback Node
💡Parallel Node
💡Decorator Node
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
5.0 / 5 (0 votes)