1 citations · 1 across the 5 of their papers we have counts for
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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…
Visuotactile-Based Learning for Insertion with Compliant Hands
Osher Azulay, Dhruv Metha Ramesh, Nimrod Curtis +1
Compared to rigid hands, underactuated compliant hands offer greater adaptability to object shapes, provide stable grasps, and are often more cost-effective. However, they introduc…
: 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…
Mono-STAR: Mono-camera Scene-level Tracking and Reconstruction
Haonan Chang, Dhruv Metha Ramesh, Shijie Geng +2
We present Mono-STAR, the first real-time 3D reconstruction system that simultaneously supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological…