2026-06-04 BREAKTHROUGHS☀ AM
Sony Ace robot beats pros at table tennis
📰 THE BRIEF
Sony AI released Ace, a seven-degree-of-freedom arm equipped with event-based cameras and trained via reinforcement learning on 10 million ball trajectories. In official matches Ace won 52 percent of points against a top-50 Japanese professional. The system updates its policy every 50 milliseconds using on-robot GPU inference.
💡 WHY IT MATTERS
You see how simulation-to-real transfer works when the reward function matches real physics. This changes your thinking from pure software prototyping to closing the loop with physical sensors and rapid policy updates.
👥 WHO'S DOING IT
Sony AI published the full results in Nature Robotics; their internal league table shows Ace maintaining a 0.71 win rate across 300 matches against regional pros in Tokyo.
⚡ TRY IT
- Clone the Ace research repository at github.com/SonyAI/Ace-RL and install the provided Docker environment.
- Launch the included MuJoCo table-tennis simulation and train a policy for 500k steps using the supplied SAC algorithm.
- Transfer the trained weights to the open-source robot arm model and record success rate on 100 simulated rallies.