Dhruv Shah
EECS Department, University of California, Berkeley
Technical Report No. UCB/EECS-2024-166
August 9, 2024
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-166.pdf
Data-driven robotics has been a very effective paradigm in the last decade. Today, we can can autonomously perform dexterous tasks like folding cloths, navigate tight hallways while avoiding collisions, and control complex dynamical systems like a quadrupedal robot walking across challenging terrains using onboard observations. But they often pose fundamental limitations that prevent them from being deployed in open-world environments, either because they make strong assumptions about the structure of their environment, require large amounts of on-robot data collection, or fail to account for semantic understanding of their surroundings. Due to these limitations, data-driven robotics approaches are still limited to simple restricted settings and not accessible to a majority of practitioners and potential applications. They still need to be hand-engineered for each separate robot, in a specific environment, to solve a specific task.
This dissertation proposes an alternate vision for intelligent robots of the future, where we can have general machine learning models that can control any robot out of the box to perform reasonable behaviors in challenging open-world environments. Inspired by the onset of foundation models of language and vision, we present a recipe for training Robot Foundation Models (RFMs) from large amounts of data, collected across different environments and embodiments, that can control a wide variety of different mobile robots by only relying on egocentric vision. We also demonstrate how such an RFM can serve as a backbone for building very capable robotic systems, that can explore dense forests, or interact with humans in their environments, or utilize sources of side information such as satellite imagery or natural language.
Finally, we propose a recipe for combining RFMs, with their knowledge of the physical world, with internet foundation models of language and vision, with their image-level semantic understanding and text-based reasoning, using a novel planning framework. This enables robotic systems to leverage the strength of internet foundation models, while also being grounded in real-world affordances and act in the real-world. We hope that this is a step towards such general purpose robotic systems that can be deployed on a wide range of robots, leverage internet-scale knowledge from pre-trained models, and serve as a foundation for diverse mobile robotic applications.
Advisors: Sergey Levine
BibTeX citation:
@phdthesis{Shah:EECS-2024-166, Author= {Shah, Dhruv}, Title= {The Foundation Model Path to Open-World Robots}, School= {EECS Department, University of California, Berkeley}, Year= {2024}, Month= {Aug}, Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-166.html}, Number= {UCB/EECS-2024-166}, Abstract= {Data-driven robotics has been a very effective paradigm in the last decade. Today, we can can autonomously perform dexterous tasks like folding cloths, navigate tight hallways while avoiding collisions, and control complex dynamical systems like a quadrupedal robot walking across challenging terrains using onboard observations. But they often pose fundamental limitations that prevent them from being deployed in open-world environments, either because they make strong assumptions about the structure of their environment, require large amounts of on-robot data collection, or fail to account for semantic understanding of their surroundings. Due to these limitations, data-driven robotics approaches are still limited to simple restricted settings and not accessible to a majority of practitioners and potential applications. They still need to be hand-engineered for each separate robot, in a specific environment, to solve a specific task.This dissertation proposes an alternate vision for intelligent robots of the future, where we can have general machine learning models that can control any robot out of the box to perform reasonable behaviors in challenging open-world environments. Inspired by the onset of foundation models of language and vision, we present a recipe for training Robot Foundation Models (RFMs) from large amounts of data, collected across different environments and embodiments, that can control a wide variety of different mobile robots by only relying on egocentric vision. We also demonstrate how such an RFM can serve as a backbone for building very capable robotic systems, that can explore dense forests, or interact with humans in their environments, or utilize sources of side information such as satellite imagery or natural language.Finally, we propose a recipe for combining RFMs, with their knowledge of the physical world, with internet foundation models of language and vision, with their image-level semantic understanding and text-based reasoning, using a novel planning framework. This enables robotic systems to leverage the strength of internet foundation models, while also being grounded in real-world affordances and act in the real-world. We hope that this is a step towards such general purpose robotic systems that can be deployed on a wide range of robots, leverage internet-scale knowledge from pre-trained models, and serve as a foundation for diverse mobile robotic applications.},} EndNote citation:
%0 Thesis%A Shah, Dhruv %T The Foundation Model Path to Open-World Robots%I EECS Department, University of California, Berkeley%D 2024%8 August 9%@ UCB/EECS-2024-166%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-166.html%F Shah:EECS-2024-166
