2026-09-19T10:22:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1446182022-08-30T03:09:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Shah, Julie A.
Githinji, Bilha-Catherine "Bilkit" W.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T15:59:57Z
2022-08-29T15:59:57Z
2022-05
2022-06-21T19:25:29.470Z
https://hdl.handle.net/1721.1/144618
Long horizon manipulation tasks are typically composed of sub-tasks with varying complexity. One phase of the task, for example, may require a continuous action space and another may be more efficiently solved using a discrete action space. Similarly, complexity in the state space may require analogous abstractions in order to apply classical planning and control methods; e.g., viewing a symbolic representation versus pixel-based representation. A common approach to addressing long horizon tasks is to develop a hierarchical system with a fixed state representation and a set of discrete and continuous action spaces to solve components of the task. However, tasks with high-dimensional state spaces present a problem for this approach where the fixed representation is ill-fit for solving certain phases of the task. This work motivates an alternative where learnt abstractions of the state space allow a hierarchical system to do coarse-to-fine reasoning of representation information to solve a task more effectively. We demonstrate a prototype of such an adaptive system and compare its performance with a system that has fixed representations. The prototype was tested in simulated table-top experiments as well as physical experiments with the Franka Emika Panda arm. The prototype outperformed the baselines in all long horizon cloth manipulation tasks by a margin of up to 20% and matched baseline performance in the rope domain.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
Model-based Control for Robot Manipulation Tasks with High-dimensional State Spaces
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>Long horizon manipulation tasks are typically composed of sub-tasks with varying complexity. One phase of the task, for example, may require a continuous action space and another may be more efficiently solved using a discrete action space. Similarly, complexity in the state space may require analogous abstractions in order to apply classical planning and control methods; e.g., viewing a symbolic representation versus pixel-based representation. A common approach to addressing long horizon tasks is to develop a hierarchical system with a fixed state representation and a set of discrete and continuous action spaces to solve components of the task. However, tasks with high-dimensional state spaces present a problem for this approach where the fixed representation is ill-fit for solving certain phases of the task. This work motivates an alternative where learnt abstractions of the state space allow a hierarchical system to do coarse-to-fine reasoning of representation information to solve a task more effectively. We demonstrate a prototype of such an adaptive system and compare its performance with a system that has fixed representations. The prototype was tested in simulated table-top experiments as well as physical experiments with the Franka Emika Panda arm. The prototype outperformed the baselines in all long horizon cloth manipulation tasks by a margin of up to 20% and matched baseline performance in the rope domain.</Abstract>
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