16 citations · 30 across the 5 of their papers we have counts for
7 papers
Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding
Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh
Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across f…
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +7
Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics,…
Disentangling Dense Multi-Cable Knots
Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan +7
Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorit…
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…
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan +5
We explore learning pixelwise correspondences between images of deformable objects in different configurations. Traditional correspondence matching approaches such as SIFT, SURF, a…
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…