Open Source

Assembly Line

RT-1: Robotics Transformer for Real-World Control at Scale

📅 Date:

✍️ Authors: Keerthana Gopalakrishnan, Kanishka Rao

🔖 Topics: Industrial Robot, Transformer Net, Open Source

🏢 Organizations: Google


Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although there have been various attempts to apply this approach to robotics, robots have not yet leveraged highly-capable models as well as other subfields.

Several factors contribute to this challenge. First, there’s the lack of large-scale and diverse robotic data, which limits a model’s ability to absorb a broad set of robotic experiences. Data collection is particularly expensive and challenging for robotics because dataset curation requires engineering-heavy autonomous operation, or demonstrations collected using human teleoperations. A second factor is the lack of expressive, scalable, and fast-enough-for-real-time-inference models that can learn from such datasets and generalize effectively.

To address these challenges, we propose the Robotics Transformer 1 (RT-1), a multi-task model that tokenizes robot inputs and outputs actions (e.g., camera images, task instructions, and motor commands) to enable efficient inference at runtime, which makes real-time control feasible. This model is trained on a large-scale, real-world robotics dataset of 130k episodes that cover 700+ tasks, collected using a fleet of 13 robots from Everyday Robots (EDR) over 17 months. We demonstrate that RT-1 can exhibit significantly improved zero-shot generalization to new tasks, environments and objects compared to prior techniques. Moreover, we carefully evaluate and ablate many of the design choices in the model and training set, analyzing the effects of tokenization, action representation, and dataset composition. Finally, we’re open-sourcing the RT-1 code, and hope it will provide a valuable resource for future research on scaling up robot learning.

Read more at Google AI Blog

How Boeing Uses Cloud Native

📅 Date:

✍️ Author: Joab Jackson

🔖 Topics: Open Source

🏢 Organizations: Boeing


“Being able to leverage the best technologists out there in the rest of the world is of great value to us strategically,” Torres, chief engineer of open source and cloud native for Boeing, said. This strategy allows Boeing to “differentiate on what we do as our core business rather than having to reinvent the wheel all the time on all of the technology.”

Like many other large companies, Boeing has created an open source office to better work with the open source community. Although Boeing is primarily a consumer of open source software, it still wants to work with the community. “We want to make sure that we have a strategy around how we contribute back to the open source community, and then leverage those learnings for inner sourcing,” he said.

Read more at The New Stack