TY - GEN
T1 - Pre-grasp planning for time-efficient and robust mobile manipulation
AU - Naik, Lakshadeep
PY - 2024/8/26
Y1 - 2024/8/26
N2 - In Mobile Manipulation (MM), navigation and manipulation actions are commonly
addressed sequentially. The time efficiency of MM can be improved by simultaneously
planning pre-grasp manipulation actions while the robot performs the navigation actions.
However, planning pre-grasp manipulation actions requires accurate 6D object poses, which
are usually available only when the robot is close to and has a clear view of the objects.
Further, pre-grasp planning with uncertain poses can lead to failures. This thesis explores
how to provide reliable object poses for pre-grasp planning along with their associated
uncertainties while the robot is still approaching the objects for grasping, and how to use these
pose estimates to plan pre-grasp actions and make informed decisions to enhance the time
efficiency and robustness of mobile manipulation.The first part of this thesis focuses on improving the object pose estimates while the
robot approaches the objects for grasping. We develop a multi-view 6D pose distribution
tracking framework that provides 6D object pose along with the associated uncertainties.
The framework compensates for limited views of the robot camera while approaching by
incorporating additional views from the stationary external cameras in the environment. The
second part of this thesis focuses on making informed decisions to reduce the risk of failures
due to uncertain object pose estimates. We develop a probabilistic inferencing framework to
determine if the uncertainty in the object pose estimate is acceptable for successfully executing
the action. The framework considers both the estimated uncertainty in the object pose and
the acceptable uncertainty for successfully completing the robotic action to determine the
likelihood of success. The final part of this thesis focuses on pre-grasp planning to improve
the time efficiency of mobile manipulation with online planning. We develop learning-based
methods for pre-grasp planning that exploit the inherent hierarchical structure of pre-grasp
planning tasks to improve sample efficiency during learning. First, we learn to plan a
pre-grasp approaching motion before grasping. Furthermore, we learn to plan the object’s
grasp sequence and base poses for grasping.The main contributions of this thesis include i) the integration of observations from
stationary external cameras in the environment with the dynamic robot camera for 6D object
pose estimation and associated uncertainty quantification, ii) a demonstration of the use of
quantified pose uncertainties to make informed robotic decisions, and iii) sample-efficient
learning by exploiting the inherent hierarchical structure of the tasks. With these contributions,
we believe this work contributes to enabling time-efficient and robust MM under uncertain
pose estimates.
AB - In Mobile Manipulation (MM), navigation and manipulation actions are commonly
addressed sequentially. The time efficiency of MM can be improved by simultaneously
planning pre-grasp manipulation actions while the robot performs the navigation actions.
However, planning pre-grasp manipulation actions requires accurate 6D object poses, which
are usually available only when the robot is close to and has a clear view of the objects.
Further, pre-grasp planning with uncertain poses can lead to failures. This thesis explores
how to provide reliable object poses for pre-grasp planning along with their associated
uncertainties while the robot is still approaching the objects for grasping, and how to use these
pose estimates to plan pre-grasp actions and make informed decisions to enhance the time
efficiency and robustness of mobile manipulation.The first part of this thesis focuses on improving the object pose estimates while the
robot approaches the objects for grasping. We develop a multi-view 6D pose distribution
tracking framework that provides 6D object pose along with the associated uncertainties.
The framework compensates for limited views of the robot camera while approaching by
incorporating additional views from the stationary external cameras in the environment. The
second part of this thesis focuses on making informed decisions to reduce the risk of failures
due to uncertain object pose estimates. We develop a probabilistic inferencing framework to
determine if the uncertainty in the object pose estimate is acceptable for successfully executing
the action. The framework considers both the estimated uncertainty in the object pose and
the acceptable uncertainty for successfully completing the robotic action to determine the
likelihood of success. The final part of this thesis focuses on pre-grasp planning to improve
the time efficiency of mobile manipulation with online planning. We develop learning-based
methods for pre-grasp planning that exploit the inherent hierarchical structure of pre-grasp
planning tasks to improve sample efficiency during learning. First, we learn to plan a
pre-grasp approaching motion before grasping. Furthermore, we learn to plan the object’s
grasp sequence and base poses for grasping.The main contributions of this thesis include i) the integration of observations from
stationary external cameras in the environment with the dynamic robot camera for 6D object
pose estimation and associated uncertainty quantification, ii) a demonstration of the use of
quantified pose uncertainties to make informed robotic decisions, and iii) sample-efficient
learning by exploiting the inherent hierarchical structure of the tasks. With these contributions,
we believe this work contributes to enabling time-efficient and robust MM under uncertain
pose estimates.
U2 - 10.21996/ygt1-3m11
DO - 10.21996/ygt1-3m11
M3 - Ph.D. thesis
PB - Syddansk Universitet. Det Tekniske Fakultet
ER -