20 citations · 55 across the 8 of their papers we have counts for
8 papers
Learning Correspondence for Deformable Objects
Priya Sundaresan, Aditya Ganapathi, Harry Zhang +1
We investigate the problem of pixelwise correspondence for deformable objects, namely cloth and rope, by comparing both classical and learning-based methods. We choose cloth and ro…
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Ryan Hoque, Kaushik Shivakumar, Shrey Aeron +7
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud r…
Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning
Aditya Ganapathi, Pete Florence, Jake Varley +3
Action representation is an important yet often overlooked aspect in end-to-end robot learning with deep networks. Choosing one action space over another (e.g. target joint positio…
VisuoSpatial Foresight for Physical Sequential 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,…
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 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…