• Matt Brown

Announcing ThoughtForge Private Beta

Updated: May 25

Today, we are excited to announce the private beta launch of ThoughtForge autonomous platform. Our goal is to make it absurdly easy to build and manage robust and truly autonomous robotic control for the real world. Robots using our technology adapt to their environment and can also work with others to achieve better results, just like humans do!


We have had years of experience building Machine Learning (ML) platforms and products. We enabled Enterprises to use robots to solve their business problems and automate their workflows and processes. For example, the calibration process for CNC machines for Siemens.

However, customers were constantly frustrated that current robotic controls are limited to doing highly precise movements in tightly controlled environments. Equipment assembly and vehicle manufacturing lines are key examples of robots operating in such environments.



The real world is dynamic and unpredictable. Existing ML techniques do not create robotic controls that perform robustly and accurately or adapt to changes in the real world. Even companies like FedEx, Amazon, and Walmart have not been able to get robotic control to satisfactorily perform tasks in their sorting facilities, warehouses, and stores.

Extensive market research and conversations with dozens of companies with similar pain points led to ThoughtForge, a simple and intuitive platform that builds autonomous systems for the real world.



At the business-level, ThoughtForge’s robust autonomy accelerates digital transformation, improves workplace safety, and lowers costs. We do this by bringing autonomy and automation to use cases and workflows in factories, warehouses, and manufacturing plants. Examples of use cases include material handling, material inspections, painting, palletizing, pick & place, and many others.


For Data Scientists and Robotic Engineers, ThoughtForge enables rapid creation and deployment, and easy management of robust and accurate autonomous systems for the real world. Our platform & patent pending framework is at least 90,000X more sample-efficient than Deep Reinforcement Learning (DRL), reducing training times to minutes or hours versus weeks to months. ThoughtForge’s programmatic approach and python based libraries make it fully compatible with your existing resources and infrastructure.

 

Customer Success Story


Note: Customer name changed to Acme due to confidentiality agreement. The details below are results from a multi-phase product engagement between Acme, a repeat customer, and ThoughtForge.


Most recently, ThoughtForge helped Acme, a F500 Oil & Gas producer, accelerate its key goal of not sending workers into potentially hazardous situations and environments such as elevated or enclosed spaces or exposing people to extreme temperatures. We enabled this with a ThoughtForge powered robotic control that performs hazardous tasks within Acme’s facilities. The data and graphs below were gathered while solving Acme’s robot use case with both ThoughtForge and existing offerings.

Advantages | Competitors

ThoughtForge

Google

Microsoft Bonsai

AWS

Training Times

Minutes

Weeks to Months

Weeks to Months

Weeks to Months

Adapts in Real-Time

Yes

No

No

No

Robust to Real World

Yes

No

No

No

Eliminates Retraining for New Environments

Yes

No

No

No

ThoughtForge’s robot models maintained 99%+ task completion accuracy under changing conditions.

Read more details about the Customer success story here.


 

Creating robotic control for factories and warehouses is the first step in our journey and our vision to create robots for everyone!


We are currently in a private beta, and would love to help your Enterprise Development and/or Data Science team build, deploy, and manage autonomous robotic controls.


Get in touch and let us help you with your use cases at info@thoughtforge.ai.

113 views0 comments

Recent Posts

See All