ACE Table Tennis Robot and the Quest to Measure Big G: Nature Podcast

ACE Table Tennis Robot and the Quest to Measure Big G

In this episode of Nature's Podcast, researchers explore an AI powered table-tennis robot named ACE that can challenge elite players, alongside a decade long blinded replication effort to determine Big G, the gravitational constant. The show also features expert commentary on robotics, metrology, and the science of measuring the universe.

• ACE uses dual sensing systems to locate the ball and its spin in real time, enabling fast, spinny returns.
• The neural network is trained in simulation with a reward function and a safety escape plan to prevent unsafe moves.
• A blind replication of Big G revealed disagreements with a French value and with the worldwide Codata consensus, illustrating ongoing challenges in precision measurement.
• Outside perspectives discuss how robotics and fundamental physics intersect with society and future technology.

Overview

The podcast opens with two intertwined stories: ACE, a six degree of freedom robot arm built to play table tennis at elite levels, and a long running effort to measure Big G, the gravitational constant, with the aim of testing one of physics' most stubborn numbers. The host, Benjamin Thompson, introduces guests from Sony AI, researchers at the University of Campinas, and metrology experts as the episode explores the frontier between AI, robotics and fundamental physics.

"Gravity is the least well understood of any of the fundamental constants of nature." - Lizzie Gibney

ACE: Perception, Reward, and Simulation

Peter Thor from Sony AI Zurich explains ACE architecture: six joints, six degrees of freedom, XY movement, and even a cup-toss serve at the end effector. The team uses fast cameras outside the playing arena to triangulate the ball's 3D position, roughly 200 times per second, and an event-based image sensor with mirrors to measure spin in real time. The neural network is trained entirely in simulation with a reward function that rewards fast, spinful returns across topspin, backspin, and other spins. Hours and hours of simulated play teach ACE to handle diverse ball trajectories and spin, culminating in a control policy that improves over time.

"The training for our control system happens exclusively in simulation." - Peter Thor

From Simulation to Real Hardware and Safety

The discussion then turns to how perception feeds into real-world control. ACE uses a custom robot platform with six joints, a linear stage, and a racket capable of >20 m/s velocity. The team built hardware that could meet their performance needs because off-the-shelf arms were insufficient. They also designed a safety mechanism that combines neural network actions with an escape plan to prevent collisions, ensuring safe interaction with human players.

"This is really a dream project for me." - Peter Thor

Trials with Humans and Expert Reactions

In human trials, ACE starts by bouncing the ball back to beginners and gradually scales up. The system has beaten a player in the world top 25 and a top 200 male; an Olympian observed a shot that was so unusual the player believed it was something a professional might adopt. The discussion then broadens to the role of sports as a proxy for training higher-level robotics skills and for safe human-robot interaction.

"Sports are really a good proxy from what we want and a very good proxy because if you want robots to work in environments where humans are living and that require interaction, you need the skills that usually you can learn in sports." - Esther Colombini

The Big G Replication: A Test for Physics

Lizzie Gibney explains why Big G is tricky: it is the least understood fundamental constant, very weak, and difficult to shield from external influences. The US National Institute of Standards and Technology (NIST) replicated a high-profile French measurement in a blinded, decade-long study to remove observer bias. The result did not match the French value and did not align with Codata's consensus either, though the researchers identified potential sources of error in the original French apparatus. The outcome underscores how difficult it is to pin down Big G with extreme precision and why Codata updates are still necessary.

"Everest, isn't it? Why does anyone climb Everest? It's because it's there. It's the challenge." - Stefan Schlamminger

Takeaways and Future Directions

The discussion ends with the reminder that Big G remains a stubborn quantity in physics, while ACE exemplifies the productive intersection of AI, perception, and robotic control. The episode links to the published replication study and related commentary, and invites listeners to explore the ongoing dialogue in metrology and robotics.