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20232025
most citedRobot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning

2 citations · 3 across the 5 of their papers we have counts for

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cs.RO2025

Empart: Interactive Convex Decomposition for Converting Meshes to Parts

Brandon Vu, Shameek Ganguly, Pushkar Joshi

Simplifying complex 3D meshes is a crucial step in robotics applications to enable efficient motion planning and physics simulation. Common methods, such as approximate convex deco…

cs.RO2025

Mobi-: Mobilizing Your Robot Learning Policy

Jingyun Yang, Isabella Huang, Brandon Vu +3

Learned visuomotor policies are capable of performing increasingly complex manipulation tasks. However, most of these policies are trained on data collected from limited robot posi…