16 citations · 20 across the 12 of their papers we have counts for
12 papers
State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
Edgar Granados, Patrick Meng, Charles Tang +4
Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of al…
Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies
Isidoros Marougkas, Dhruv Metha Ramesh, Joe H. Doerr +4
Object insertion under tight tolerances () is an important but challenging assembly task as even small errors can result in undesirable contacts. Recent effo…
Kinodynamic Trajectory Following with STELA: Simultaneous Trajectory Estimation & Local Adaptation
Edgar Granados, Sumanth Tangirala, Kostas E. Bekris
State estimation and control are often addressed separately, leading to unsafe execution due to sensing noise, execution errors, and discrepancies between the planning model and re…
: Sampling-Based Kinodynamic Replanning and Feedback Control over Approximate, Identified Models of Vehicular Systems
Aravind Sivaramakrishnan, Sumanth Tangirala, Dhruv Metha Ramesh +2
This paper aims to increase the safety and reliability of executing trajectories planned for robots with non-trivial dynamics given a light-weight, approximate dynamics model. Scen…
: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space
Ewerton R. Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala +3
Estimating the region of attraction () for a robot controller is essential for safe application and controller composition. Many existing methods require a closed-form e…
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…
Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers
Aravind Sivaramakrishnan, Edgar Granados, Seth Karten +2
This paper aims to improve the path quality and computational efficiency of sampling-based kinodynamic planners for vehicular navigation. It proposes a learning framework for ident…
Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space
Michal Kleinbort, Edgar Granados, Kiril Solovey +3
We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores…