Tech

Sony AI's Ace Robot Outperforms Pro Athletes via Reinforcement Learning Breakthrough

May 14, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from ai.sony

What happened

Sony AI published in Nature on Ace, an autonomous bipedal robot using advanced force-torque sensors and model-based reinforcement learning. Ace beats professional athletes in agile tasks like dynamic running and jumping. It achieves 10-20% higher success rates in real-world physics simulations turned physical trials.

Why it matters

This demonstrates RL with precise sensors unlocks real-world robotics beyond simulations. Shift thinking from sim-only training to sensor-rich hardware loops. Workflow now integrates MuJoCo sims with real hardware for rapid iteration.

Who's doing it

Sony AI's Team led by Peter K. Allen. Ace outperformed human pros in 5/7 athletic benchmarks, including 2m high jumps and 5m/s sprints with 95% reliability in unstructured environments.

Try it

1. Install Stable Baselines3 and MuJoCo via pip install stable-baselines3[mujoco]; download Humanoid-v4 env from Gymnasium.
2. Train PPO agent with custom force-torque observations using sb3's PPO('MlpPolicy', env, verbose=1).
3. Evaluate on 100 episodes; expect 20-50% score improvement over baselines toward pro-athlete metrics like velocity>4m/s. URL: stable-baselines3.readthedocs.io.

Read the original at ai.sony