7 citations · 33 across the 47 of their papers we have counts for
6 papers · 1 filter
Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling
Daniel Lawson, Ahmed H. Qureshi
Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning to robotics. From another perspective, under know…
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…
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Ruiqi Ni, Ahmed H. Qureshi
Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for lear…
Robot Active Neural Sensing and Planning in Unknown Cluttered Environments
Hanwen Ren, Ahmed H. Qureshi
Active sensing and planning in unknown, cluttered environments is an open challenge for robots intending to provide home service, search and rescue, narrow-passage inspection, and…
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…