Google introduces new AI methods to develop advanced robots

New Delhi, Jan 5 The Google DeepMind robotics team has introduced new AI-based systems based on large language models (LLMs) to help develop better multi-tasking robots for our daily use.

The tech giant unveiled AutoRT, SARA-RT and RT-Trajectory systems to improve real-world robot data collection, speed, and generalisation.

“We’re announcing a suite of advances in robotics research that bring us a step closer to this future. AutoRT, SARA-RT, and RT-Trajectory build on our historic Robotics Transformers work to help robots make decisions faster, and better understand and navigate their environments,” the Google DeepMind team said in a statement.

AutoRT harnesses the potential of large foundation models which is critical to creating robots that can understand practical human goals.

AutoRT combines large foundation models such as a LLM or a Visual Language Model (VLM), and a robot control model (RT-1 or RT-2) to create a system that can deploy robots to gather training data in novel environments.

“In extensive real-world evaluations over seven months, the system safely orchestrated as many as 20 robots simultaneously, and up to 52 unique robots in total, in a variety of office buildings, gathering a diverse dataset comprising 77,000 robotic trials across 6,650 unique tasks,” the team informed.

The Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) system converts Robotics Transformer (RT) models into more efficient versions.

“The best SARA-RT-2 models were 10.6 per cent more accurate and 14 per cent faster than RT-2 models after being provided with a short history of images. We believe this is the first scalable attention mechanism to provide computational improvements with no quality loss,” said the DeepMind team.

When the team applied SARA-RT to a state-of-the-art RT-2 model with billions of parameters, it resulted in faster decision-making and better performance on a wide range of robotic tasks.

Another model called RT-Trajectory hich automatically adds visual outlines that describe robot motions in training videos.

RT-Trajectory takes each video in a training dataset and overlays it with a 2D trajectory sketch of the robot arm’s gripper as it performs the task.

“These trajectories, in the form of RGB images, provide low-level, practical visual hints to the model as it learns its robot-control policies,” said Google.

When tested on 41 tasks unseen in the training data, an arm controlled by RT-Trajectory more than doubled the performance of existing state-of-the-art RT models: it achieved a task success rate of 63 per cent compared with 29 per cent for RT-2.

“RT-Trajectory can also create trajectories by watching human demonstrations of desired tasks, and even accept hand-drawn sketches. And it can be readily adapted to different robot platforms,” according to the team.

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