Bi-Level Belief Space Search for Assembly Tasks

Contact-rich manipulation tasks, such as assembly, require a robot to reason about both the geometric relationship between parts as well as the dynamical relationship between the forces the robot exerts and the motion of the parts. The application of forces enables the robot to reduce its uncertaint...

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Bibliographic Details
Main Author: Chintalapudi, Sahit
Other Authors: Kaelbling, Leslie P.
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/154156
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author Chintalapudi, Sahit
author2 Kaelbling, Leslie P.
author_facet Kaelbling, Leslie P.
Chintalapudi, Sahit
author_sort Chintalapudi, Sahit
collection MIT
description Contact-rich manipulation tasks, such as assembly, require a robot to reason about both the geometric relationship between parts as well as the dynamical relationship between the forces the robot exerts and the motion of the parts. The application of forces enables the robot to reduce its uncertainty by purposefully contacting the environment, a crucial skill in real-world domains where state is not fully observed. In this thesis, a planner is introduced that reasons over both gripper poses and joint stiffnesses, trading off motion generation to reach an objective and force production to manage uncertainty. Our planner performs a greedy optimization over stiffness and learns a model of the relationship between control output and goal achievement to bias the pose search. This planner is validated on a peg-in-hole insertion task in simulation and the real world and a puzzle assembly task in simulation. We measure the effects of solving for stiffnesses and generating robust gripper poses in terms of the uncertainty our planner can address.
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spelling mit-1721.1/1541562024-04-17T03:01:45Z Bi-Level Belief Space Search for Assembly Tasks Chintalapudi, Sahit Kaelbling, Leslie P. Lozano-Perez, Tomas Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Contact-rich manipulation tasks, such as assembly, require a robot to reason about both the geometric relationship between parts as well as the dynamical relationship between the forces the robot exerts and the motion of the parts. The application of forces enables the robot to reduce its uncertainty by purposefully contacting the environment, a crucial skill in real-world domains where state is not fully observed. In this thesis, a planner is introduced that reasons over both gripper poses and joint stiffnesses, trading off motion generation to reach an objective and force production to manage uncertainty. Our planner performs a greedy optimization over stiffness and learns a model of the relationship between control output and goal achievement to bias the pose search. This planner is validated on a peg-in-hole insertion task in simulation and the real world and a puzzle assembly task in simulation. We measure the effects of solving for stiffnesses and generating robust gripper poses in terms of the uncertainty our planner can address. S.M. 2024-04-16T19:04:17Z 2024-04-16T19:04:17Z 2024-02 2024-04-08T16:53:38.084Z Thesis https://hdl.handle.net/1721.1/154156 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Chintalapudi, Sahit
Bi-Level Belief Space Search for Assembly Tasks
title Bi-Level Belief Space Search for Assembly Tasks
title_full Bi-Level Belief Space Search for Assembly Tasks
title_fullStr Bi-Level Belief Space Search for Assembly Tasks
title_full_unstemmed Bi-Level Belief Space Search for Assembly Tasks
title_short Bi-Level Belief Space Search for Assembly Tasks
title_sort bi level belief space search for assembly tasks
url https://hdl.handle.net/1721.1/154156
work_keys_str_mv AT chintalapudisahit bilevelbeliefspacesearchforassemblytasks