16 citations · 20 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
USHER: Unbiased Sampling for Hindsight Experience Replay
Liam Schramm, Yunfu Deng, Edgar Granados +1
Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for…
Morse Graphs: Topological Tools for Analyzing the Global Dynamics of Robot Controllers
Ewerton R. Vieira, Edgar Granados, Aravind Sivaramakrishnan +3
Understanding the global dynamics of a robot controller, such as identifying attractors and their regions of attraction (RoA), is important for safe deployment and synthesizing mor…
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…