2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
COAST: Constraints and Streams for Task and Motion Planning
Brandon Vu, Toki Migimatsu, Jeannette Bohg
Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towa…
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
Jennifer Grannen, Yilin Wu, Brandon Vu +1
Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is imme…
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
Jingyun Yang, Max Sobol Mark, Brandon Vu +3
The pre-train and fine-tune paradigm in machine learning has had dramatic success in a wide range of domains because the use of existing data or pre-trained models on the internet…