2 citations · 4 across the 4 of their papers we have counts for
6 papers
CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
Abhinav K. Keshari, Hanwen Ren, Ahmed H. Qureshi
Robot grasping is an actively studied area in robotics, mainly focusing on the quality of generated grasps for object manipulation. However, despite advancements, these methods do…
Co-design of Embodied Neural Intelligence via Constrained Evolution
Zhiquan Wang, Bedrich Benes, Ahmed H. Qureshi +1
We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our ma…
Model-free Neural Lyapunov Control for Safe Robot Navigation
Zikang Xiong, Joe Eappen, Ahmed H. Qureshi +1
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks. While a model-free DRL algorithm can s…
NeRP: Neural Rearrangement Planning for Unknown Objects
Ahmed H. Qureshi, Arsalan Mousavian, Chris Paxton +2
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of o…
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
Mayur J. Bency, Ahmed H. Qureshi, Michael C. Yip
Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot's actuation or configuration…
Deeply Informed Neural Sampling for Robot Motion Planning
Ahmed H. Qureshi, Michael C. Yip
Sampling-based Motion Planners (SMPs) have become increasingly popular as they provide collision-free path solutions regardless of obstacle geometry in a given environment. However…