144 citations · 484 across the 36 of their papers we have counts for
6 papers · 2 filters
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects
Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown +2
There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consistently fail to grasp challenging…
Accelerating Grasp Exploration by Leveraging Learned Priors
Han Yu Li, Michael Danielczuk, Ashwin Balakrishna +2
The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service. Data-driven grasping policies have achieved success in lear…
Untangling Dense Knots by Learning Task-Relevant Keypoints
Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan +7
Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We…
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +5
Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furth…
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan +10
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior wo…
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Ryan Hoque, Daniel Seita, Ashwin Balakrishna +6
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks,…