2026-09-19T03:02:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1474912023-01-20T03:14:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Shah, Julie
Cuellar, Alex
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-01-19T19:53:57Z
2023-01-19T19:53:57Z
2022-09
2022-09-16T20:23:34.624Z
https://hdl.handle.net/1721.1/147491
One core problem of robot viability in many sectors is retrainability; if a robot’s task can change without changing code, automation becomes feasible for a wider set of applications. To advance robot retrainability, this thesis will introduce a learning from demonstrations (LfD) framework allowing a robot to learn and execute tasklevel plans in spatially complex environments. To achieve this goal, we introduce a propositional logic framework to encode spatial relationships between objects and an inference scheme to identify important relationships between defined object classes. Finally, we present a search-based algorithm to synthesize required class relationships into a task-level plan. As a representative problem for this of context, we focus on the problem of box packing, wherein the robot must learn specific rules surrounding how to place objects in a box according to a demonstrator’s wishes.
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Inference and Task Planning over Spatially Complex Problems
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>One core problem of robot viability in many sectors is retrainability; if a robot’s task can change without changing code, automation becomes feasible for a wider set of applications. To advance robot retrainability, this thesis will introduce a learning from demonstrations (LfD) framework allowing a robot to learn and execute tasklevel plans in spatially complex environments. To achieve this goal, we introduce a propositional logic framework to encode spatial relationships between objects and an inference scheme to identify important relationships between defined object classes. Finally, we present a search-based algorithm to synthesize required class relationships into a task-level plan. As a representative problem for this of context, we focus on the problem of box packing, wherein the robot must learn specific rules surrounding how to place objects in a box according to a demonstrator’s wishes.
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.</Abstract>
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