Tech

Sony AI's Ace Robot Outperforms Pro Athletes in Real-World Tasks via Reinforcement Learning

May 13, 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 the Ace system, an autonomous bipedal robot using advanced force-torque sensors and model-based reinforcement learning. Ace beats professional athletes in dynamic ball-catching tasks across 10+ variations. It achieves 80% success rate in unpredictable environments versus humans' 60%.

Why it matters

This demonstrates hybrid RL with physics simulation for real-world robotics, bridging sim-to-real gaps. Shift your thinking to sensor fusion for robustness in dynamic settings. Workflow now includes sim training before hardware deployment, accelerating iteration.

Who's doing it

Sony AI's robotics division deployed Ace prototypes, outperforming human baselines by 30% in agility tests. Their prior work on adaptive RL achieved 95% task success in lab simulations.

Try it

1. Install Isaac Gym via NVIDIA's GitHub (github.com/NVIDIA-Omniverse/IsaacGym).
2. Set up a bipedal robot env, train with PPO RL algorithm targeting force-torque rewards for 10M steps.
3. Transfer to real hardware via domain randomization; expect 70%+ sim-to-real success. URL: https://ai.sony/news/sony-ai-announces-breakthrough-research-in-real-world-artificial-intelligence-and-robotics.

Read the original at ai.sony