In addition to training future players, the technology could expand the capabilities of other humanoid robots, such as for search and rescue
MIT engineers are getting in on the robotic ping pong game with a powerful, lightweight design that returns shots with high-speed precision.
The new table tennis bot comprises a multijointed robotic arm that is fixed to one end of a ping pong table and wields a standard ping pong paddle. Aided by several high-speed cameras and a high-bandwidth predictive control system, the robot quickly estimates the speed and trajectory of an incoming ball and executes one of several swing types — loop, drive, or chop — to precisely hit the ball to a desired location on the table with various types of spin.
In tests, the engineers threw 150 balls at the robot, one after the other, from across the ping pong table. The bot successfully returned the balls with a hit rate of about 88 percent across all three swing types. The robot’s strike speed approaches the top return speeds of human players and is faster than that of other robotic table tennis designs.
Now, the team is looking to increase the robot’s playing radius so that it can return a wider variety of shots. Then, they envision the setup could be a viable competitor in the growing field of smart robotic training systems.
Beyond the game, the team says the table tennis tech could be adapted to improve the speed and responsiveness of humanoid robots, particularly for search-and-rescue scenarios, and situations in which a robot would need to quickly react or anticipate.
“The problems that we’re solving, specifically related to intercepting objects really quickly and precisely, could potentially be useful in scenarios where a robot has to carry out dynamic maneuvers and plan where its end effector will meet an object, in real-time,” says MIT graduate student David Nguyen.
Nguyen is a co-author of the new study, along with MIT graduate student Kendrick Cancio and Sangbae Kim, associate professor of mechanical engineering and head of the MIT Biomimetics Robotics Lab. The researchers will present the results of those experiments in a paper at the IEEE International Conference on Robotics and Automation (ICRA) this month.
Precise Play
Building robots to play ping pong is a challenge that researchers have taken up since the 1980s. The problem requires a unique combination of technologies, including high-speed machine vision, fast and nimble motors and actuators, precise manipulator control, and accurate, real-time prediction, as well as higher-level planning of game strategy.
“If you think of the spectrum of control problems in robotics, we have on one end manipulation, which is usually slow and very precise, such as picking up an object and making sure you’re grasping it well. On the other end, you have locomotion, which is about being dynamic and adapting to perturbations in your system,” Nguyen explains. “Ping pong sits in between those. You’re still doing manipulation, in that you have to be precise in hitting the ball, but you have to hit it within 300 milliseconds. So, it balances similar problems of dynamic locomotion and precise manipulation.”
Ping pong robots have come a long way since the 1980s, most recently with designs by Omron and Google DeepMind that employ artificial intelligence techniques to “learn” from previous ping pong data, to improve a robot’s performance against an increasing variety of strokes and shots. These designs have been shown to be fast and precise enough to rally with intermediate human players.
“These are really specialized robots designed to play ping pong,” Cancio says. “With our robot, we are exploring how the techniques used in playing ping pong could translate to a more generalized system, like a humanoid or anthropomorphic robot that can do many different, useful things.”
Game Control
For their new design, the researchers modified a lightweight, high-power robotic arm that Kim’s lab developed as part of the MIT Humanoid — a bipedal, two-armed robot about the size of a small child. The group is using the robot to test various dynamic maneuvers, including navigating uneven terrain, jumping, running, and doing backflips, aiming to deploy such robots for search-and-rescue operations.
Each of the humanoid’s arms has four joints, or degrees of freedom, each controlled by an electrical motor. Cancio, Nguyen, and Kim built a similar robotic arm, adapted for ping pong with an extra degree of freedom in the wrist to control the paddle.
The team fixed the arm to one end of a standard ping pong table and set up high-speed motion capture cameras to track incoming balls. They developed control algorithms that predict, using math and physics, the paddle orientation and speed needed to return each type of swing — loop, drive, or chop.
Using three computers to process camera images and convert predictions into motor commands, the robot responded in real time. In tests, it returned 150 consecutive balls with nearly equal success across all swings: 88.4% for loop, 89.2% for chop, and 87.5% for drive. Later tests showed the robot could hit balls at 20 meters per second — faster than existing systems.
The average strike speed was 11 meters per second, approaching human advanced players (21–25 m/s). Further tuning pushed the robot’s speed to 19 meters per second (42 mph).
“Some of the goal of this project is to say we can reach the same level of athleticism that people have,” Nguyen says. “And in terms of strike speed, we’re getting really, really close.”
They also implemented aiming capabilities. The robot can now hit a ball to a preset location using trajectory prediction. However, since it’s fixed to the table, it can only cover a crescent-shaped area. The team plans to mount it on a gantry or mobile base for greater range.
“A big thing about table tennis is predicting the spin and trajectory of the ball, given how your opponent hit it, which is information that an automatic ball launcher won’t give you,” Cancio says. “A robot like this could mimic real-game conditions to help humans train.”
This research is supported in part by the Robotics and AI Institute.
Source: Jennifer Chu | MIT News
