Humanoid Robots Boost Efficiency via On-Hardware RL
Scobleizer · x · 2026-07-11
This post introduces a training method closer to deploying "truly practical robots": not just imitating human demos, but repeatedly performing production tasks on real hardware and learning from its own successes and failures.
The author highlights several key points:
- Traditional "imitation learning" only replicates demonstrations, capping the robot's capabilities by the demonstrator's limits and failing to learn the cost of failure.
- The approach here allows humanoids to practice continuously in real tasks, with reinforcement learning running directly on the physical robot, 24/7.
- As a result, the success rate of grasping and handover tasks increased from 80% to 98%, and failures dropped 10-fold.
- Throughput for dual-arm tote handling more than doubled, with a success rate nearing 99%.
The author views this as the robotics sector beginning to mirror the "predictable, compute-driven performance curves" of the LLM era: rather than one-off demos, the system continuously improves as the robot works more. Crucially, once these fleets scale, every robot will continuously generate training data, creating a closed loop where performance keeps improving with use.
Related event: Humanoid Robots Shift to Real-World Reinforcement Learning(3 posts)→
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