Boston Dynamics Executives Offer a Reality Check: The Humanoid Industry Is Still in ‘Phase One’

Editor’s Note: Boston Dynamics released a candid discussion today detailing the engineering realities behind their new electric Atlas. We break down the key insights below, and you can watch the full video at the bottom of this post.

In a landscape saturated with accelerated timelines and claims of autonomous deployment, Boston Dynamics released a candid "webinar" today that serves as both a strategic roadmap and a sober reality check for the humanoid robotics industry.

Featuring Alberto Rodriguez, Director of Robot Behavior for Atlas, and Aya Durbin, Product Lead for Humanoid Applications, the discussion offers a glimpse into the internal philosophy driving the development of the all-electric Atlas. While competitors like Figure are touting months-long operational streaks at BMW, Boston Dynamics is taking a more measured stance, characterizing the entire sector as being in the early stages of hardware validation.

The "Phase One" Admission

Perhaps the most striking moment of the discussion came when Rodriguez, a former MIT faculty member, broke down the lifecycle of a robotic product into three distinct phases.

Phase one is building the machine and solving hardware reliability. Phase two involves sustained customer deployment, where engineers learn "the other bitter lesson"—that their assumptions about what the customer needed were likely wrong. Phase three is scaling to thousands of units, where long-tail reliability issues emerge.

I would say where all humanoids are today... is still in phase one

"I would say where all humanoids are today... is still in phase one," Rodriguez stated. "We're sort of working really hard and really fast to get to phase two, but I think it's very clear that we're still in that phase."

This assessment provides important context to the broader activities of Hyundai Motor Group (HMG), Boston Dynamics' parent company. HMG recently announced a massive KRW 125.2 trillion (approx. $92.7 billion) investment plan, a significant portion of which is earmarked for "Physical AI" and robotics infrastructure.

The investment includes a new "Physical AI Application Center" to test robots before deployment, aligning perfectly with Rodriguez's description of the rigor required to graduate from "phase one."

The Internal Debate: Why Legs?

As the humanoid form factor gains traction, a divergence in design philosophy has emerged. Some companies use wheeled bases for stability and speed. Aya Durbin acknowledged that this trade-off is a subject of frequent debate, even inside Boston Dynamics.

"I get this question from customers all the time. I get this question from my family all the time," Durbin said regarding the necessity of bipedal locomotion. "It's definitely been an internal debate."

Durbin and Rodriguez argued that while legs add mechanical complexity, they are essential for the long-term vision of a general-purpose machine. Rodriguez noted that advances in control theory have made dynamic stability "an easy problem today," no longer the bottleneck it once was.

"Legs give you a slimmer body which allows you to go to more places in more constrained environments," Rodriguez explained, noting that a biped can execute discrete contact changes to turn faster than a wheeled robot in tight spaces.

Manufacturing as "Manipulation Complete"

The discussion also shed light on why Boston Dynamics, along with competitors like Figure and Neura Robotics, is laser-focused on automotive manufacturing as a beachhead.

Rodriguez described automotive assembly not just as a market opportunity, but as a technical benchmark. He termed the environment "manipulation complete"—meaning if a robot can master the complex, dexterous tasks required to assemble a car, it can likely handle almost any other manipulation task in the physical world.

He cited the specific example of "fishing" a bolt out of a bowl, orienting it, and driving it into a car body while bracing with a free hand. This level of dexterity addresses the high variability of modern manufacturing, where a single line might produce five different car models with thousands of part variations—a complexity that hard automation has historically failed to address economically.

The Death of the "Pyramid"

The conversation highlighted a fundamental shift in how the "brains" of these robots are being built. Rodriguez described the old method as a "pyramid" of layered algorithms—processing raw pixels into features, then objects, then maps, and finally actions.

"The problem with that is... any change you do, you have to change a million things in the system," Rodriguez said.

Instead, the team is moving toward end-to-end neural networks, or "Physical AI," a term HMG has begun using to describe its core infrastructure investment. This approach relies on "pre-training" (a base model of common sense and physics) followed by "post-training" (on-the-job learning via demonstration and correction).

"This is the 'other bitter lesson'—that the customer is always right and you were probably wrong," Rodriguez noted, emphasizing that real-world deployment is less about perfect code and more about a system that can learn from its own mistakes in the field.

A Long Game Strategy

While Figure has aggressively marketed its milestones, such as a 5-month run at a BMW plant, Boston Dynamics appears to be leveraging the deep pockets and industrial patience of Hyundai Motor Group to solve fundamental reliability problems before scaling.

With HMG planning to build a dedicated "Robotics Manufacturing and Foundry" facility, the infrastructure to mass-produce Atlas is being laid. However, as today's discussion made clear, the software and reliability challenges required to make that hardware useful are far from solved.

"We want to make sure that as we do research... our hardware is there to support it," Durbin concluded. "And then if we have different form factors in the future, that's even better."

---

Watch the discussion below: