@inproceedings{bonatti2022pact,
author = {Bonatti, Rogerio and Vemprala, Sai and Ma, Shuang and Vieira Frujeri, Felipe and Chen, Shuhang and Kapoor, Ashish},
title = {PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training},
organization = {IEEE},
booktitle = {IROS 2023},
year = {2022},
month = {September},
abstract = {Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise and prior knowledge. Inspired by large pre-trained language models, this work introduces a paradigm for pre-training a general purpose representation that can serve as a starting point for multiple tasks on a given robot. We present the Perception-Action Causal Transformer (PACT), a generative transformer-based architecture that aims to build representations directly from robot data in a self-supervised fashion. Through autoregressive prediction of states and actions over time, our model implicitly encodes dynamics and behaviors for a particular robot. Our experimental evaluation focuses on the domain of mobile agents, where we show that this robot-specific representation can function as a single starting point to achieve distinct tasks such as safe navigation, localization and mapping. We evaluate two form factors: a wheeled robot that uses a LiDAR sensor as perception input (MuSHR), and a simulated agent that uses first-person RGB images (Habitat). We show that finetuning small task-specific networks on top of the larger pretrained model results in significantly better performance compared to training a single model from scratch for all tasks simultaneously, and comparable performance to training a separate large model for each task independently. By sharing a common good-quality representation across tasks we can lower overall model capacity and speed up the real-time deployment of such systems.},
publisher = {International Conference on Intelligent Robots and Systems},
url = {http://approjects.co.za/?big=en-us/research/publication/pact-perception-action-causal-transformer-for-autoregressive-robotics-pre-training/},
note = {MoBT 17.1 / MoBIP 17.1},
}