A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
Name
Simeonov-asimeono-SM-EECS-2022-thesis.pdf
Description
Thesis PDF
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6.52 MB
Format
Adobe PDF
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Author(s)
Simeonov, Anthony
Advisor(s)
Agrawal, Pulkit
Rodriguez, Alberto
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i.e. without prior object models. Our method plans in the space of object subgoals and frees the planner from reasoning about robot-object interaction dynamics by relying on a set of generalizable manipulation primitives. We show that for rigid bodies, this abstraction can be realized using low-level manipulation skills that maintain sticking contact with the object and represent subgoals as 3D transformations. To enable generalization to unseen objects and improve planning performance, we propose a novel way of representing subgoals for rigid-body manipulation and a graph-attention based neural network architecture for processing point-cloud inputs. We experimentally validate these choices using simulated and real-world experiments on the YuMi robot. Results demonstrate that our method can successfully manipulate new objects into target configurations requiring long-term planning. Overall, our framework realizes the best of the worlds of task-and-motion planning (TAMP) and learning-based approaches.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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