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2022年12月13日 · We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties.

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RT-1 takes a short sequence of images and a task description in natural language as input and outputs an action for the robot to execute at each time step.

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2022年12月13日 · 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 …

This is the completion of google's rt-1 project code and can run directly. You can view the google source code here: robotics_transformer. RT-1 dataset: robotics_transformer_dataset. …

To train RT-1 in distributed mode with 4 GPUs: The training configuration is stored in train_config.json. The training configuration for RT-1 includes various parameters that …

This repository contains complete PyTorch implementation for RT-1 based on the paper: RT-1 Paper and implementation of RT-1 model by maruya24's RT-1: maruya24's RT-1 GitHub. Our implementation complete the training, validation pipeline, and soon-to-come evaluation pipeline. Try our implementation in Colab.

2023年7月10日 · Learning-based robotic manipulation has advanced to a new level in the past few years, thanks to large-scale real-robot data [1, 2].