30 citations · 30 across the 5 of their papers we have counts for
6 papers · 1 filter
Learning Multi-step Robotic Manipulation Policies from Visual Observation of Scene and Q-value Predictions of Previous Action
Sulabh Kumra, Shirin Joshi, Ferat Sahin
In this work, we focus on multi-step manipulation tasks that involve long-horizon planning and considers progress reversal. Such tasks interlace high-level reasoning that consists…
Learning Robotic Manipulation Tasks via Task Progress based Gaussian Reward and Loss Adjusted Exploration
Sulabh Kumra, Shirin Josh, Ferat Sahin
Multi-step manipulation tasks in unstructured environments are extremely challenging for a robot to learn. Such tasks interlace high-level reasoning that consists of the expected s…
Robotic Grasping using Deep Reinforcement Learning
Shirin Joshi, Sulabh Kumra, Ferat Sahin
In this work, we present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach r…
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
Sulabh Kumra, Shirin Joshi, Ferat Sahin
In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from n-channel image of the scene…
Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution
Sulabh Kumra, Ferat Sahin
In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy fro…
Robotic Grasp Detection using Deep Convolutional Neural Networks
Sulabh Kumra, Christopher Kanan
Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been wide…