CAREER: Intelligent Manipulation in the Real World via Modularity and Abstraction

University Of Texas At Austin, Austin TX

Investigators

Abstract

This Faculty Early Career Development (CAREER) project seeks to enable intelligent robots to see, think, and act in real-world tasks. Recent years have witnessed great strides in deep learning for robotics. Yet, state-of-the-art manipulation algorithms still fall short of generalization and robustness for widespread deployment. It poses a major obstacle to real-world applications from industrial automation to general-purpose household robots. This research will accelerate the deployment of adept and reliable robots from research settings to real-world environments. It will make fundamental contributions to transformative robotics technologies in practical applications, including manufacturing, healthcare, and home assistance. As part of the project, the education plan will integrate this research into undergraduate and graduate education through course development and research mentorship. The outreach activities will include an open-source software initiative and K-12 educational programs for high school students. The overall objective of this project is to build new algorithms and tools for intelligent robot manipulation in the real world. The crux of this research is a suite of algorithms that build effective abstractions and harness these abstractions to synthesize sophisticated behaviors. Concretely, the research has three intimately connected research themes: 1) developing perceptual abstraction with object-centric representations, 2) building motor abstraction with sensorimotor skills, and 3) modeling complex manipulation tasks with these abstractions. The algorithms will be rigorously evaluated at the module and system levels and deployed on the physical robot hardware to perform a spectrum of household tasks. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.