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20192025
most citedA Survey on the Integration of Machine Learning with Sampling-based Motion Planning

16 citations · 20 across the 8 of their papers we have counts for

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10 papers · 1 filter

cs.RO2025

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…

cs.RO2025

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…

cs.RO2024

: 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…

cs.RO2023

: 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…

cs.RO2023

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…

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…