UK startup Humanoid launches reinforcement learning system to improve robot manipulation
UK-based robotics and AI company Humanoid has introduced KinetIQ Ascend, the company’s reinforcement learning approach designed to reach 99.9 percent manipulation reliability at human speed and beyond. KinetIQ Ascend builds on the previously announced KinetIQ platform with trial-and-error learning, helping the company’s robots improve directly on industrial tasks. The new system was tested on several […]
The rapid advancements in AI, particularly reinforcement learning, are enabling significant breakthroughs in robotic automation, making sophisticated manipulation increasingly feasible outside lab environments.
Achieving human-speed, high-reliability manipulation is a critical stepping stone for commercially viable humanoid robots, moving them from research curiosities to practical industrial tools.
The introduction of KinetIQ Ascend signifies a tangible step towards overcoming the manipulation reliability bottleneck for humanoid robots, potentially accelerating their deployment in diverse industrial tasks.
- · Humanoid (company)
- · Industrial automation sector
- · Logistics and manufacturing companies
- · Embodied AI developers
- · Labour-intensive manual assembly industries
- · Current generation robotic arm manufacturers (if they can't adapt)
- · Companies relying on low-skill, high-volume labour
Humanoid robots will gain broader adoption in industrial tasks requiring intricate manipulation.
This improved capability will drive down manufacturing costs and increase production efficiency across various sectors.
The enhanced practicality of humanoid robots will likely spur further investment and competition, accelerating the development of general-purpose humanoid intelligence and broader societal integration.
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