# KinetIQ Ascend uses real-world reinforcement learning to boost humanoid robot manipulation throughput and success rate

- Source: HUMANOID
- Published: 2026-06-25T12:23:40.000Z
- RoboHot: https://robohot.news/en/items/q1f5tr1bhyhb7mypjw1ya12oh
- Original: https://thehumanoid.ai/kinetiq-ascend-toward-100-reliable-manipulation-and-superhuman-speed/

## Summary

HUMANOID has launched KinetIQ Ascend, an end-to-end visual reinforcement learning method that operates under production deployment conditions, extending its KinetIQ framework to let robots improve manipulation skills through trial and error rather than relying solely on human demonstration; the company says this is the first such method publicly demonstrated on production-line humanoid robot manipulation.

## Why it matters

The original text provides specific throughput and success-rate figures for three industrial tasks and notes a pattern of predictable performance improvement with training duration, making it useful for assessing the practical value of real-world reinforcement learning on humanoid robots.