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20172022
most citeddRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning

93 citations · 185 across the 21 of their papers we have counts for

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28 papers · 1 filter

cs.RO202216 cited

A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

Troy McMahon, Aravind Sivaramakrishnan, Edgar Granados +1

Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, effective in practice for many robotic systems. It is o…

cs.RO2022

Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees

Ewerton R. Vieira, Aravind Sivaramakrishnan, Yao Song +5

This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot contro…

cs.RO2022

Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations

Junchi Liang, Bowen Wen, Kostas Bekris +1

This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demons…

cs.RO20221 cited

Persistent Homology for Effective Non-Prehensile Manipulation

Ewerton R. Vieira, Daniel Nakhimovich, Kai Gao +3

This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of pers…

cs.RO2022

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Andrew S. Morgan, Kaiyu Hang, Bowen Wen +2

Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand's kinematic topology. Finger gaiting is…

cs.RO2022

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

Seth Karten, Aravind Sivaramakrishnan, Edgar Granados +2

This paper aims to improve the path quality and computational efficiency of kinodynamic planners used for vehicular systems. It proposes a learning framework for identifying promis…