* Accel's Sameer Gandhi joins Mind Robotics' board

* Mind Robotics, a Rivian spin-out, operates with the EV

maker as partner

* Experts warn commercializing advanced robotics requires

vast data for model training

By Akash Sriram

March 11 (Reuters) - Mind Robotics, a startup spun out

of EV maker Rivian, said on Wednesday it has raised

$500 million in a Series A funding co-led by Accel and

Andreessen Horowitz.

The industrial robotics company was valued at $2 billion,

according to a person close to the deal.

The funding, which is one of the largest Series A rounds for

a robotics company, comes at a time when manufacturers are

increasingly dealing with labor shortages and are under pressure

to update aging production lines.

The Palo Alto-headquartered company is building a full-stack

platform of foundation models, purpose-built robots and

deployment infrastructure to automate industrial and

manufacturing tasks at scale.

Accel partner Sameer Gandhi will join the company's board as

part of the deal, which is expected to close later this month,

and follows a $115 million seed round led by Eclipse Capital

late last year.

Mind Robotics, founded by Rivian CEO and founder RJ

Scaringe, was spun out of the EV company in November. Rivian

operates as a partner and major shareholder, providing Mind

Robotics with data for training its models and an environment to

launch the technology.

The startup aims to address the gap in industrial automation

by developing robots that can handle tasks requiring human-like

dexterity, adaptability and physical reasoning.

Scaringe has said the company intends to focus on more

traditional factory robot designs, rather than the much-hyped

humanoid robots that have garnered significant attention.

Tesla and Nvidia ( NVDA )-backed Figure AI are

among many robotics companies that are building robots in a

human-like form factor.

However, industry experts and executives have cautioned that

commercializing advanced robotics remains a long road, as

training the foundation models that power these systems demands

vast amounts of data, a resource that is difficult to accumulate

at scale.