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PhD Thesis Proposal


Alex LaGrassa PhD Student Robotics Institute,
Carnegie Mellon University
Monday, March 13
12:00 pm to 1:30 pm
NSH 3305
Enabling Data-Efficient Real-World Model-Based Manipulation by Estimating Preconditions for Inaccurate Models

This thesis explores estimating and reasoning about model deviation in robot learning for manipulation to improve data efficiency and reliability to enable real-robot manipulation in a world where models are inaccurate but still useful. Existing strategies are presented for improving planning robustness with low amounts of real-world data by an empirically estimated model precondition to guide a  model-based planner or use a model-free skill.

Then, approaches to reduce the amount of real-robot data required to compute reliable plans with inaccurate models are described. The first uses model disagreement to guide exploration. The second addresses how to efficiently collect data to learn a dynamics model and corresponding model precondition. Lastly, the thesis suggests research directions to explore in order to scale model deviation estimates on high-dimensional data.

Thesis Committee Members:
Oliver Kroemer, Chair
Maxim Likhachev
David Held
Dmitry Berenson, University of Michigan

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