Meta Acquires Robotics AI Startup As It Makes The Push Into Humanoid Machines






Meta has purchased Assured Robot Intelligence (ARI), a startup company that’s building artificial intelligence for robots in order to “address critical challenges” in “high-value labor markets.” The company is already working on robot hardware and AI in-house, but a spokesperson told Bloomberg that ARI “will bring a deep expertise in how [it] can design [its] models and frontier capabilities for robot control and self-learning to whole-body humanoid control.” They didn’t reveal the financial details of the acquisition. 

In a post on X, ARI co-founder Xiaolong Wang said that from the start, they knew achieving his company’s goals meant “training a truly general-purpose physical agent.” He continued that they now believe the agent will be humanoid and that “scaling will come from learning directly from human experience.” Meta, he added, has access to the “key components needed to make this vision possible.” Wang, his co-founders Xuxin Cheng and Lerrel Pinto, and the ARI team will be joining Meta’s Superintelligence Labs. Pinto also co-founded Fauna Robotics but left the company last year before it was acquired by Amazon for its own humanoid robot project. 

Meta CTO Andrew Bosworth said back in 2025 that the company is aiming to create software that other companies can license, similar to what Google does with Android. “Software is the bottleneck,” he explained. He said the plan was to start with developing software that can power a dexterous hand and then building out the technology from there. In addition to Meta and Amazon, Tesla has also been working on humanoid robots for quite a while now. The automaker decided to stop producing Model S and X cars earlier this year and to convert their production space in the company’s Fremont factory to manufacture Optimus humanoid robots instead.





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What is a Mapplet?

A Mapplet is an object that can be reused. It will be created in the Mapplet Designer. It includes a set of transformations and allows us to reuse transformation logic in more than one Mapping. Let us understand Mapplets with an example. Consider you have some fact tables which need a series of dimension keys. We can create a Mapplet with a series of Lookup transformations to find every dimension key. We can use this Mapplet in every fact table Mapping instead of recreating the same search logic in every Mapping. As a reusable transformation, any changes to the Mapplet are inherited from every instance of the Mapplet. A Reusable transformation refers to any rule or transition logic defined on the data that will be transformed from source to destination via Mapping, and it can be provided more than once with various methods and logic. it can be used for other transformations.

Mapplet is applicable to mapped data. The source object and the target object will be defined and stored in the database. A logic to be transformed is defined by offering a Mapplet Input that transmits the data from Mapping to Mapplet. After that, a desired Mapplet output transform transmits the Mapplet data to Mapping. So the Input-Output transformations will evolve into cyclic Mapping, transaction logic and reused data without any impact on the data.

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Why Mapplets?

The Mapplets are created to serve two primary purposes:

Whenever we load new data into a table, some space occurs in the source system, and whenever a new transformation is required to be configured on each Mapping. Mapplets are created to prevent this repetition of the creation of the transformation expressions and to end the unwanted space that is formed in the source system. 
On each workflow that is running in the system, a batch id will be formed according to the timestamp of the session using a Mapplet.

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Informatica Mapplet features: 

  • A Mapplet is used to define key data source definitions as well as a source qualifier for providing the data for Mapping.
  • A Mapplet may not have the source definition of the data. Mapplet may accept this data as well from a mapping process via Mapplet input ports.
  • A Mapplet may have several transformations. 
  • The data may be transformed into several pipelines. A Mapplet may include several groups of output ports/groups which may be connected to one another via various pipelines in the Mapping.


Mapplet Components:

For using a Mapplet in Mapping, we must configure the Mapplet for input and output. Apart from the transformation logic we are configuring, a Mapplet includes the following components:

  • Mapplet ports
  • Mapplet input
  • Mapplet output 
Mapplet Ports:

It will be available only in the Mapping Designer. The Mapplet ports comprise the input ports of the input transformations and the output ports of the output transformations.

Mapplet Input:

The Mapplet input may come from the source definition or from the Input transformation within the Mapplet. Several pipelines can be created in a Mapplet. Utilize several source definitions as well as the input transformations or source qualifiers. Alternatively, you can use a mixture of input transformations and source definitions.

Mapplet Output:

The Mapplet output consists of transformation for passing the data through the Mapplet into the Mapping. At Least one output transformation is included in the Mapplet that includes at least one connected port in the Mapplet. Every port connected in the output transformation displays as a Mapplet output port in the Mapping. Every output transformation in the Mapplet is displayed as the output group in the Mapping. The output group may transmit data to several pipelines within the Mapping.

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Creation of a Mapplet in Informatica:

The creation of Mapplets involves three steps.

  • First, we must add, connect and then configure the transformations using a transformation logic.
  • Then we need to save the Mapplet using a unique name.
  • Now we can use this Mapplet in the Mapping.
     

When dealing with the Mapplets, mistakes can be avoided by:

  • One of the best ways to avoid mistakes in Mapplets is to comment on every input as well as output transformation that is made in Mapplets so that they will remind us why we have made them.
  • Avoid making modifications to source data datatype, data accuracy or select ports that are connected in the I/O transformation, from the passive to active Mapplet.
     

Follow the rule and guidelines while adding transformations to the Mapplet:

  • When you are using the sequence generator transformation, you should use the reusable sequence generator transformation.
  • When you are using the stored procedure transformation, you should configure the stored procedure Type as Normal.
  • The PowerMart, 3.5 style LOOKUP functions, cannot be included in a Mapplet.
     

Following are the objects that cannot be included in a Mapplet:

  • COBOL sources
  • Normalizer transformations
  • Other Mapplets
  • Target definitions
  • XML sources
  • XML source qualifier transformations
  • Pre and post-session stored procedures
  • Non-reusable sequence generator transformation
     

While reusable shortcuts and transformations in the Mapplet can be used, for protecting the Mapplet validity, use a copy of the transformation rather. Reusable transformations or shortcuts inherit modifications to their original transformations. This could disable the Mapplet and Mapping, which utilizes the Mapplet.

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Conclusion:

In this blog, we have learned about Mapplets, why we use Mapplets, Mapplets features, components of a Mapplet and how to create a Mapplet. We hope you found this information helpful!. For more blogs like this, stay tuned to HKR Trainings. 

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