Tech

Sony AI's Project Ace: Finally, Real-World Robotics That Rivals Elite Humans

May 9, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from ai.sony

What happened

Sony AI published Project Ace, a fully autonomous robotic system trained via reinforcement learning in simulated and real-world environments. It excels in table tennis, achieving rally durations competitive with professional players: over 100 strokes per match. The system uses a vision-language-action model integrated with high-fidelity physics simulation for zero-shot transfer to reality.

Why it matters

This demonstrates end-to-end reinforcement learning with sim-to-real transfer, bridging the gap between simulation and physical deployment. Change your thinking: simulation-first training accelerates robotics development by orders of magnitude. In your workflow, adopt MuJoCo or Isaac Gym for scalable RL training before hardware tests.

Who's doing it

Sony AI's robotics division deployed Project Ace, enabling a robot arm to sustain table tennis rallies averaging 137 strokes against elite human players, surpassing prior benchmarks by 30%.

Try it

1. Install Sony's open-source tiered learning framework from GitHub: git clone https://github.com/SonyAI-Ace/project-ace.git; pip install -r requirements.txt.
2. Train in simulation using their RL pipeline: python train.py --task table_tennis --sim mujoco, targeting 100+ episode rewards.
3. Transfer to real robot via domain randomization; expect 80% sim performance on hardware like Franka Emika Panda arm.

Read the original at ai.sony