The race to build practical humanoid robots faces an unexpected hurdle: getting motion systems from the lab to the factory floor. While artificial intelligence advances in weeks, the mechanical components that make robots move take months to develop and years to manufacture at scale, creating a significant gap that's holding back the entire industry.

Why Is Manufacturing Speed the Real Challenge?

Humanoid robot makers like Boston Dynamics, Figure AI, and Unitree Robotics have demonstrated impressive prototypes that can perform complex tasks. But translating those working models into products that can be manufactured reliably and affordably remains a formidable obstacle. The mismatch between software iteration cycles and hardware production timelines is often the true bottleneck, not the conceptual design of the robots themselves.

At RoboBusiness 2026, scheduled for October 20 and 21 in Santa Clara, California, Yoshi Umeno, global director of business development for robotics at Kollmorgen, will address this challenge directly. His session, titled "A Joint-by-Joint Guide to Humanoid Motion," will explore how manufacturers can think about robot motion not as a one-size-fits-all problem but as a zone-by-zone architecture challenge.

"By reframing humanoid motion as a zone-by-zone architecture problem rather than a one-size-fits-all approach, Umeno will outline the unique demands of each body zone and the challenges of scaling for production," according to the conference description.

Yoshi Umeno, Global Director of Business Development for Robotics at Kollmorgen

What Does a Zone-by-Zone Approach Mean for Robot Development?

Rather than designing a single motion system that works everywhere on a humanoid body, manufacturers need to recognize that different body regions, such as the arms, legs, torso, and head, have distinct mechanical requirements. The shoulders need different actuators and control systems than the wrists or ankles. This modular thinking can help companies identify the right motion partners and co-engineering support to accelerate the path from prototype to volume production.

Kollmorgen, a brand of Regal Rexnord, specializes in motion control components including actuators, motors, drives, and precision parts. The company has worked with leading humanoid robot makers globally and understands the practical challenges of scaling these systems.

How to Evaluate Motion Partners for Humanoid Development

• Comprehensive Solutions: Look for partners who can provide integrated motion systems across multiple body zones rather than point solutions for individual joints.
• Co-Engineering Support: Choose partners willing to work closely with your team to customize components for your specific robot architecture and manufacturing constraints.
• Production Expertise: Prioritize partners with proven experience scaling motion hardware from prototype quantities to high-volume manufacturing without compromising reliability or cost.
• System-Level Performance: Evaluate how motion components interact across the entire robot, not just individual joint performance in isolation.

What's Happening in the Broader Robot Learning Space?

While manufacturing remains a challenge, other robotics companies are making progress on the software side. Generalist AI, a Cambridge-based startup founded by former Google DeepMind and Boston Dynamics researchers, has demonstrated robots that can learn new tasks from brief instructional videos without task-specific training. This represents a significant shift toward more adaptable, general-purpose robots.

The startup's approach focuses on teaching robots about physics and how the world works, similar to how human children learn through observation and experimentation. In one demonstration, a robot watched a video of someone unzipping a purse and removing money, then successfully performed the same task with a different purse, even switching from its right gripper to its left when it couldn't grab the bills initially.

"This is exactly the kind of thing people were really excited about with GPT-3. You could take that model and just prompt it to do a new task and it would have a real shot at doing it," said Pete Florence, Generalist AI cofounder and CEO.

Pete Florence, Cofounder and CEO at Generalist AI

Generalist AI's approach involves collecting large amounts of high-quality training data using special gloves with attached cameras that workers use to perform various tasks. The company has gathered hundreds of these data-collection devices and deployed them to workers in Mexico and elsewhere.

However, the technology still has limitations. Robots trained by Generalist AI complete tasks successfully about 59 percent of the time on average, well below the 99 percent reliability needed for commercial deployment. The company has built its AI models entirely from scratch rather than relying on open-source language models, giving it more control over the training process but also requiring significant internal research effort.

Danfei Xu, a roboticist at Georgia Tech familiar with Generalist's work, noted that the startup stands out among companies pursuing more general robot models. "They have pushed this to the extreme, and they've done a really good job executing," Xu said, adding that "they are excellent roboticists, and they have done really good science".

The convergence of better robot learning software and improved manufacturing processes could accelerate the timeline for practical humanoid robots entering real-world applications. But as the industry moves forward, the manufacturing challenge highlighted by Kollmorgen remains a critical factor that will determine which companies successfully scale their robots from impressive prototypes to reliable, affordable products.