16 citations · 22 across the 11 of their papers we have counts for
11 papers
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…
PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter
Dhruv Metha Ramesh, Aravind Sivaramakrishnan, Shreesh Keskar +3
In critical applications, including search-and-rescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, parti…
: 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…
Roadmaps with Gaps over Controllers: Achieving Efficiency in Planning under Dynamics
Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados +2
This paper aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers. Offline, a system-spe…
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…
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…
Towards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning
Aravind Sivaramakrishnan, Zakary Littlefield, Kostas E. Bekris
Planning for systems with dynamics is challenging as often there is no local planner available and the only primitive to explore the state space is forward propagation of controls.…