7 papers
GradTac: Spatio-Temporal Gradient Based Tactile Sensing
Kanishka Ganguly, Pavan Mantripragada, Chethan M. Parameshwara +3
Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck…
Grasping in the Dark: Zero-Shot Object Grasping Using Tactile Feedback
Kanishka Ganguly, Behzad Sadrfaridpour, Pavan Mantripragada +3
Grasping and manipulating a wide variety of objects is a fundamental skill that would determine the success and wide spread adaptation of robots in homes. Several end-effector desi…
Deep Differentiable Grasp Planner for High-DOF Grippers
Min Liu, Zherong Pan, Kai Xu +2
We present an end-to-end algorithm for training deep neural networks to grasp novel objects. Our algorithm builds all the essential components of a grasping system using a forward-…
Computational Tactile Flow for Anthropomorphic Grippers
Kanishka Ganguly, Behzad Sadrfaridpour, Cornelia Fermüller +1
Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate.…
Generating Grasp Poses for a High-DOF Gripper Using Neural Networks
Min Liu, Zherong Pan, Kai Xu +2
We present a learning-based method for representing grasp poses of a high-DOF hand using neural networks. Due to redundancy in such high-DOF grippers, there exists a large number o…
Extracting Contact and Motion from Manipulation Videos
Konstantinos Zampogiannis, Kanishka Ganguly, Cornelia Fermuller +1
When we physically interact with our environment using our hands, we touch objects and force them to move: contact and motion are defining properties of manipulation. In this paper…