When the Practice Partner Is a Robot
A tennis player sends the ball across the net.
Her opponent analyzes the trajectory, moves into position, and sends another shot back.
Then it adjusts again.
Except this opponent is not another athlete.
It is a robot.
On September 24, 2026, an embodied tennis robot called TENNIIX ULTRA MAX demonstrated that kind of interaction at the Billie Jean King Cup Finals in Shenzhen. The system tracked the ball, adjusted its position, and varied the shots it delivered while rallying with professional player Wang Qiang. Just five days later, Tennis Australia announced that AI-powered training company PongBot would become the Official Ball Machine of the Australian Open and supply technology to the National Tennis Academy beginning with AO27.
For students, these developments can look futuristic.
For K–8 educators, they are an invitation to teach STEM.
A sports robot brings together sensors, computer science, artificial intelligence, engineering, mathematics, mechanics, and data inside one system students can immediately understand. A robot needs to recognize what is happening, decide what to do, move correctly, evaluate the result, and try again.
In other words, it needs to solve problems.
That makes sports robotics a natural extension of the way STEM Sports® already approaches learning. The STEM Sports ebook explains that connecting STEM concepts to sports can make challenging ideas more approachable by giving students active, real-world situations in which those concepts actually matter. STEMSportsEbook
Robotics adds a new question to that approach:
How would you teach a machine to play the game?
A Robot Is a System, Not Just a Machine
Students may picture a robot as a metal person that walks and talks.
Robotic systems are much broader.
The National Institute of Standards and Technology describes robots as “systems of systems.” To complete a task, a robot must perceive its environment through sensors and algorithms, determine what is happening, plan or adapt its actions, and then execute those actions through movement or manipulation.
For a sports robot, that process can be simplified into three stages:
Sense → Decide → Act
A tennis robot might:
Sense: Where is the ball going?
Decide: Where should I move and what shot should I send next?
Act: Move into position and deliver the ball.
A soccer robot might sense the ball and other players, calculate its location on the field, choose whether to move, pass, or shoot, and then control its motors to execute the decision.
Suddenly, coding and robotics are no longer abstract.
They are part of the game.
Sensors Give Robots Their Version of Senses
Humans gather information through sight, hearing, touch, and other senses.
Robots rely on sensors.
Cameras can provide visual information. Distance sensors can determine how close an object is. Position sensors can help track movement. Force or pressure sensors can provide information about physical contact.
The tennis robot demonstrated at the Billie Jean King Cup combined robotic mobility with an AI training system that tracked ball trajectory, spin, landing location, and player position before adjusting its response.
That creates an immediate classroom connection.
Before students think about programming a robot, they need to decide:
What information would the robot need?
Classroom Investigation: Give the Robot Eyes
Recommended grades: K–8
STEM focus: sensing, coordinates, observation and data
Create a simple court or field grid using floor tape, cones, or graph paper.
One student is the “robot.” Another student serves as the sensor.
Place a ball somewhere on the grid.
The sensor cannot simply point to the ball. Instead, the sensor provides information such as:
- Row 3, Column 5
- Two squares forward
- One square right
The student acting as the robot must move based only on the data received.
For younger learners, use arrows or colors.
Older students can use coordinate pairs or calculate distances between points.
Then introduce an error.
What happens if the sensor reports the wrong location?
Students quickly discover that a robot’s decision can only be as useful as the information entering the system.
That is a foundational idea in robotics, data science, and computer science.
Coding Turns Information Into Decisions
A robot does not simply collect information.
It needs instructions for what to do with it.
Students can explore this through basic if/then logic.
For example:
IF the ball lands on the left side,
THEN move left.
IF the ball is farther than three squares away,
THEN move two spaces.
IF an obstacle is directly ahead,
THEN change direction.
These simple rules introduce algorithms—the ordered processes computers use to solve problems.
As the challenge becomes more complicated, students discover why programming real robots is difficult.
What if the ball is moving?
What if another robot blocks the path?
What if two rules apply at the same time?
What if the sensor is uncertain?
Now coding becomes problem-solving rather than memorizing commands.
Classroom Investigation: Program the Robot Coach
Recommended grades: 3–8
STEM focus: coding logic, algorithms and debugging
Give teams a fictional training robot and a simple goal:
Return a tennis ball to one of three target zones.
Students create a flowchart showing how their robot should make decisions.
For example:
- Detect the ball.
- Identify its location.
- Choose the nearest target.
- Move into position.
- Return the ball.
- Evaluate whether the target was reached.
Then introduce new conditions.
The target moves.
One area becomes blocked.
A sensor provides incomplete information.
Students revise—or debug—the algorithm.
No computer is necessary to practice computational thinking.
The thinking comes first.
Robots Also Have to Master Movement
Deciding where to go is only part of the problem.
A robot also has to physically get there.
Motors and other actuators turn electrical instructions into movement. Engineers must consider balance, acceleration, stopping distance, traction, stability, weight, and the mechanical structure of the robot itself.
Sports put those challenges under pressure because the environment changes constantly.
RoboCup offers one of the clearest examples.
On July 5, 2026, two teams of humanoid robots played what the RoboCup Federation described as the first full 11-vs-11 humanoid robot soccer match on real hardware. The robots operated autonomously, meaning they had to perceive, decide, move, and coordinate without a person directly controlling every action.
RoboCup’s Humanoid Soccer League uses soccer specifically as a research environment for challenges including vision, locomotion, learning, behavior control, and team coordination.
Humans make those tasks look easy.
Try asking a machine to walk toward a moving ball without falling over.
The engineering challenge becomes obvious.
Classroom Investigation: Design the Robot Athlete
Recommended grades: 3–8
STEM focus: engineering design, constraints, mechanics and prototyping
Give students a fictional challenge:
A robot needs to collect a tennis ball, soccer ball, or foam ball and deliver it to a target.
Students choose how the robot should move.
Should it use:
- Wheels?
- Legs?
- Tracks?
- A combination?
Then determine how it will interact with the ball.
Will it push?
Kick?
Launch?
Carry?
Students sketch the robot and label its sensors, moving parts, power source, and control system.
Add engineering constraints:
- Maximum size
- Limited budget
- Limited number of motors
- Indoor or outdoor use
- Uneven terrain
- A narrow turning radius
Teams present their designs and explain their decisions.
The objective is not to produce one “correct” robot.
It is to show that engineering involves trade-offs.
A wheeled robot may move quickly on a smooth court but struggle on uneven terrain. A legged robot may handle obstacles but require more complicated balance control.
Every solution creates another question.
Team Sports Make Robotics Even Harder
One robot following a ball is challenging.
Eleven robots playing soccer together create an entirely different problem.
Now the robots need to consider teammates.
Who should move toward the ball?
Who should defend?
Where is open space?
What happens if two robots try to perform the same job?
RoboCup uses sports precisely because these environments force researchers to solve complex problems involving autonomous coordination and decision-making.
For students, this is an excellent introduction to multi-agent systems—situations in which multiple independent systems must operate together.
The idea also connects naturally to human sports.
Teams succeed when individuals understand their own role while responding to everyone else.
Robots need a version of that same teamwork.
Classroom Investigation: Robot Teamwork Challenge
Assign groups of students different robot roles:
- Defender
- Ball collector
- Passer
- Scorer
Students cannot talk during the challenge.
Instead, each role receives a small set of programmed rules.
For example:
If the ball enters Zone A, the defender moves toward it.
If the passer receives the ball, send it toward Zone C.
Teams run the system, observe what goes wrong, and change their rules.
Students are practicing coding logic, systems thinking, communication, and debugging—without writing a single line of software.
Robotics Shows Why Feedback Matters
One of the most important ideas in engineering is the feedback loop.
A robot acts.
Then it needs information about what happened.
Did it reach the correct location?
Did the ball land where expected?
Did another player move?
Should the next action change?
The process becomes:
Sense → Decide → Act → Measure → Adjust
That cycle looks a lot like the engineering design process students already use.
Design.
Test.
Collect evidence.
Improve.
Test again.
That is one reason sports work so well as a robotics environment. Outcomes are immediate.
The robot either reached the ball or it did not.
The pass reached the target or it missed.
The route worked or an obstacle exposed a problem.
Failure is not the end of the activity.
It becomes data.
Students Do Not Need Expensive Robots to Learn Robotics
A robotics lesson does not require a humanoid robot standing in the gym.
Teachers can build the thinking first.
Paper algorithms can teach programming logic.
Grid activities can introduce navigation.
Cardboard prototypes can teach mechanical design.
Human “robots” can follow student-written instructions.
Schools that already have programmable classroom robots can extend the activities into line following, target navigation, or ball-moving challenges.
What matters is that students experience the full system.
They should have opportunities to decide what the robot needs to sense, create rules for how it responds, test the system, identify failures, and improve the design.
That process mirrors the inquiry-based approach already central to STEM Sports.
The STEM Sports Playbook emphasizes that sports-based learning can connect physical experiences to STEM concepts and careers, while building critical thinking, problem-solving, creativity, teamwork, and collaboration.
Why Robotics Can Be Such a Powerful Engagement Tool
The attached STEM Sports case studies provide an especially relevant proof point.
At F.K. White Middle School, Sandra Hayes reports that the excitement students developed through hands-on STEM Sports lessons carried beyond her classroom: some students joined the middle school robotics team and later continued pursuing robotics in high school.
In East Cleveland, Lisa Longino has seen students become eager to explore concepts like velocity and heart rate through active sports-based lessons. She reports gains in math, reading, attendance, and engagement, while students increasingly carry what they learn in PE into later science and math conversations.
Sports robotics combines both of those worlds.
Students get the familiar context and physical connection of sports with the coding, engineering, and problem-solving challenge of robotics.
Connecting Sports Robotics to STEM Sports Curriculum
STEM Tennis provides the most immediate connection to this week’s news. Students already explore force, court dimensions, serving, energy, and advancements in tennis, with technology tools including a radar gun and Billie Jean King’s Eye Coach Training System. A robotics extension can ask students how an automated training partner would sense a serve, predict where a ball will land, and select its response.
STEM Soccer gives students an ideal bridge into RoboCup. Its curriculum includes goal-line technology, ball systems, field geometry, measurement, probability, and the Engineering Design Process. Students can extend those ideas by imagining how an autonomous soccer robot would locate itself, identify the ball, and navigate the field.
STEM Multi-Sport already blends technology and physical investigation across basketball, football, soccer, and volleyball. Modules include shot tracking, quarterback technology, serving technology, and adaptive technology—all useful jumping-off points for discussing sensors and automated systems.
The STEM Sports Digital Curriculum adds another natural layer by pairing physical learning with digital tools and reporting, giving educators an opportunity to connect movement with the broader world of data and technology.
Career Connections: Who Builds the Robot Athlete?
Sports robotics can introduce students to careers they may never realize exist around athletics:
- Robotics engineer
- Mechanical engineer
- Software developer
- AI or machine-learning engineer
- Computer vision engineer
- Mechatronics engineer
- Sensor engineer
- Controls engineer
- Data scientist
- Human-robot interaction researcher
- Product designer
- Sports technology specialist
That connection matters because students who love sports do not have to become professional athletes to build careers in the sports world.
Sandra Hayes emphasizes this idea with her students: athletes depend on people who build stadiums, operate technology, support health, and manage the systems around the game.
Robotics adds one more possibility.
A student may not become the person hitting the tennis ball.
They may become the engineer who teaches a robot how to return it.
The Future of Sports Can Become a Classroom Today
Sports robots are no longer limited to science-fiction movies.
They are rallying with professional tennis players.
They are entering elite training environments.
They are playing soccer in autonomous teams.
And they are forcing engineers to solve some of robotics’ hardest problems: perception, balance, navigation, decision-making, communication, and real-time control.
That is what makes this topic so valuable for K–8 STEM.
Students can begin with a game they understand and uncover a complex system beneath it.
A camera becomes a sensor.
A strategy becomes an algorithm.
A motor becomes an actuator.
A mistake becomes debugging.
A teammate becomes part of a multi-agent system.
And a sports challenge becomes an engineering problem.
Robots may be learning how to play sports.
But for educators, sports can help students learn how robots work.