9 citations · 18 across the 2 of their papers we have counts for
6 papers
Deep Tactile Experience: Estimating Tactile Sensor Output from Depth Sensor Data
Karankumar Patel, Soshi Iba, Nawid Jamali
Tactile sensing is inherently contact based. To use tactile data, robots need to make contact with the surface of an object. This is inefficient in applications where an agent need…
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,…
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,…
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Daniel Seita, Aditya Ganapathi, Ryan Hoque +11
Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexit…
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Daniel Seita, Nawid Jamali, Michael Laskey +6
A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. W…